# Interactive effects of management and temperature anomalies on CO2 fluxes recorded over 18 years in a temperate upland grassland system

Bruna Winck, Katja Klumpp, Juliette Bloor

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Bruna Winck, Katja Klumpp, Juliette Bloor. Interactive effects of management and temperature anomalies on CO2 fluxes recorded over 18 years in a temperate upland grassland system. Agricultural and Forest Meteorology, 2025, 362, pp.110343. 10.1016/j.agrformet.2024.110343. hal-04827801

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# Interactive effects of management and temperature anomalies on CO₂ fluxes recorded over 18 years in a temperate upland grassland system

Bruna Winck *, Katja Klumpp, Juliette M.G. Bloor

Université Clermont Auvergne, INRAE, VetAgro-Sup, UREP, 5 Chemin de Beaulieu, F-63100 Clermont-Ferrand, France

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## ARTICLE INFO

**Keywords:**

Climate change

Ecosystem respiration

Flux anomalies

Grazing

GPP

Management intensity

## ABSTRACT

Temperature anomalies such as heat waves and cold spells are increasingly common, posing threats to ecosystem functioning and delivered services. Although temperature anomalies have been shown to limit broad-scale patterns of carbon sequestration, fine-scale studies on CO₂ fluxes and temperature anomalies are limited and interactions between temperature anomalies and agricultural management remain poorly understood. Using long-term data on grassland CO₂ fluxes and air temperature (2003-2021), we investigated the effects of temperature anomalies (cold, warm, extreme cold, extreme warm) on gross primary productivity and ecosystem respiration in adjacent upland mesic grasslands subjected to either extensive or intensive cattle grazing management treatments. Both warm and cold temperature anomalies occurred throughout the study period, with 40 days per year on average classed as colder or warmer compared to normal. Irrespective of management treatment and flux type, we found that temperature anomalies had a significant effect on the magnitude of CO₂ fluxes, the relationship between air temperature and CO₂ fluxes, and the occurrence of anomalous flux events. The magnitude of CO₂ fluxes was most affected by temperature anomalies at the start of the growing season, and fluxes were generally more sensitive to warm rather than cold temperature anomalies. However, cold temperature anomalies promoted asynchrony between C uptake and loss. Temperature-CO₂ flux relationships were upregulated by warm temperature anomalies in spring but down-regulated in summer and autumn, with greater reductions in flux process rates in the intensive management treatment. Nevertheless, temperature anomalies were not found to be a strong driver of flux anomalies in this study system. Collectively, our results show that grassland management interacts with the timing and nature of temperature anomalies on CO₂ fluxes and suggest that low grassland management intensity may buffer CO₂ fluxes against warm air temperature anomalies in cool temperate systems, with implications for adaptation strategies for grasslands under climate change.

## 1. Introduction

Climate anomalies such as drought and extreme temperature events have become increasingly common over the last twenty years and are expected to increase in frequency with ongoing climate change (IPCC 2021, NOAA 2022). Such climate anomalies induce physiological stress in organisms and have the potential to drive dramatic and/or irreversible changes in terrestrial ecosystem structure and function, impacting ecosystem health and delivered services (Malhi et al., 2020). Concerns over drought have given rise to a wealth of empirical and modelling studies addressing ecosystem productivity and CO₂ fluxes in relation to rainfall anomalies (e.g., Frank et al., 2015; Jiao et al., 2022; Yang et al., 2016). At the same time, temperature anomalies have been shown to limit broad-scale patterns of C sequestration through reductions in C

assimilation and increases in ecosystem respiration (Duffy et al., 2021; Pan et al., 2020), yet fine-scale studies on CO₂ fluxes and temperature anomalies remain rare. Improving our understanding of the impacts of temperature anomalies on the carbon cycle is critical for quantifying carbon-cycle climate feedback and for greater accuracy of climate change assessments (Zscheischler et al., 2014).

Grassland ecosystems play a crucial role in global C cycling as they store approximately one third of the total terrestrial carbon stocks, acting as an important belowground sink of C (Bai and Cotrufo, 2022). Recent meta-analyses indicate that small but prolonged increases in temperatures (1°C to 3°C) generally promote grassland productivity, soil respiration and ecosystem respiration (R<sub>eco</sub>), although the magnitude of stimulatory effects may vary depending on pedoclimatic conditions, seasonal variations, and management (Chen et al., 2022; Shi et al., 2022;

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: bru.winck@gmail.com (B. Winck).</footnote>
Wang et al., 2019). In the case of temperature anomalies, considered here as statistically-unusual episodes beyond the bounds of normal variability (Reichstein et al., 2013), both extremely high and low temperatures are known to impact carbon cycling directly (via changes in plant physiological functions, developmental processes and/or growth) and indirectly (via changes in resource availability or plant susceptibility to natural enemies) (Frank et al., 2015; Juurakko et al., 2021). Available experimental evidence suggests that mesic grassland CO₂ fluxes and biomass production are particularly sensitive to positive temperature anomalies during the growing season, even in the absence of drought (Li et al., 2023; Yuan et al., 2018, but see De Boeck et al., 2016).

Changes in management intensity are a key factor known to alter the balance between grassland C emissions and sinks and are commonly perceived as a lever for greenhouse gas mitigation in a warming world (Barneze et al., 2022; Chang et al., 2021). Previous studies on low-level experimental warming have reported synergistic effects of combined warming and N addition on grassland CO₂ fluxes (Barneze et al., 2022; Shi et al., 2022), whereas other studies have shown that positive effects of warming can offset negative effects of grazing on R<sub>eco</sub> (Zhou et al., 2019). To date, few studies have explicitly examined interactions between grassland management and temperature anomalies on CO₂ flux data measured *in natura*, but the existence of such interactions has been suggested by recent work focusing on periods after management events (Michel and Seneviratne, 2022; Rogger et al., 2022). Grassland management intensification due to increased fertilization or grazing has the potential to modify grassland responses to temperature anomalies via associated changes in vegetation composition, plant C fixation rates, microbial community structure and soil C pools (Rumpel et al., 2015). Analysis of satellite imagery and long-term climate data suggests that lower management intensity, i.e., semi-natural versus agricultural grasslands, promotes the stability of grassland biomass production under temperature anomalies (De Keersmaecker et al., 2015), likely due to the higher local diversity and soil food-web complexity associated with these ecosystems (Bloor et al., 2021; Bomfim et al., 2021; Oliveira et al., 2022; Winck et al., 2019). In theory, the same diversity-based mechanisms should enhance the stability of grassland fluxes under temperature anomalies in extensively-managed grasslands, but differences in the climate-buffering capacity of grasslands across gradients of management intensity remain to be demonstrated.

In this study, we used data from an 18-year grassland management trial in the French Massif Central region to examine interactions between temperature anomalies and grassland management (intensive or extensive) on CO₂ fluxes (R<sub>eco</sub>; daytime gross primary production, GPP). Adjacent grassland paddocks with independent eddy covariance measurements provide a unique opportunity to directly test for climate x management interactions on CO₂ fluxes. Moreover, long-term CO₂ flux measurements are a valuable tool for assessing ecosystem metabolism over a range of timescales and have the potential to capture rare events that can be episodic or extreme (Baldocchi, 2020; Frank et al., 2015). Recent work at this grassland study site has shown that air temperature is the principal driver of daily variation in R<sub>eco</sub> and GPP (Winck et al., 2023). Here we evaluate the presence and effects of warm and cold temperature anomalies on grassland CO₂ fluxes at different temporal scales (month, season). Specifically, we hypothesize that: i) magnitude of CO₂ flux response to temperature anomalies varies depending on the season, ii) low grassland management intensity buffers CO₂ flux against air temperature anomalies. We also investigate whether temperature anomalies modify the relationship between CO₂ fluxes and temperature in the different management treatments.

## 2. Material and methods

### 2.1. Study site and experimental design

The Laqueuille grassland management trial was established in 2003

to address the effects of management intensity (stocking rate, N inputs) on the C balance of an upland grassland ecosystem in the French Massif Central (1040 m asl; 45°38′N, 2°44′E; Allard et al. 2007). The study site has been under permanent grass cover since the 1950s, and the vegetation is dominated by grasses (Dactylis glomerata, Holcus mollis, Poa pratensis and Agrostis capillaris) with the presence of legumes (Trifolium repens), and other forbs (e.g. Cerastium fontanum). The local climate is classified as Cfb (Temperate oceanic climate) according to the Köppen classification; mean annual temperature and rainfall are 8.05°C and 1100 mm, respectively (25-year average, monitoring data from the INRAe Climatik platform, https://agroclim.inrae.fr/climatik/). During the study period (2003-2021), mean annual T<sub>air</sub> ranged from 6°C to 9°C and total precipitation from 638 mm to 1308 mm. Mean monthly T<sub>air</sub> ranged from 0.6°C in winter to 16°C in summer (Fig. S1). The soil is an Andosol (20 % clay, 53 % silt and 27 % sand) with carbon content ranging from 100 g kg⁻¹ to 104.1 g kg⁻¹ (180 g kg⁻¹ organic matter) and average bulk soil density of 0.55 g cm⁻³ in the topsoil layer (0 - 10 cm).

The site is divided into two paired experimental fields and has been managed by cattle grazing under low (Extensive; 3.4 ha; livestock unit = 0.51 ha⁻¹ yr⁻¹) and high (Intensive; 2.8 ha; livestock unit = 1.1 ha⁻¹ yr⁻¹) animal stocking rate, with continuous grazing during the plant growing season (late April to late October) and mineral fertiliser application for the high grazing treatment with 200 kg ha⁻¹ yr⁻¹ of N, applied as ammonium nitrate in 3 splits (April, June, August). Vegetation characteristics for each experimental field in the period from 2003 to 2008 are presented in Klumpp et al. (2011).

### 2.2. Eddy covariance measurements and flux data processing

Continuous measurements of surface-atmosphere exchanges of CO₂ and H₂O have been carried out at the study site since 2003 using an Eddy Covariance (EC) tower installed at a height of 2 m in each experimental field. Each tower is equipped with a high frequency sonic anemometer (Model Solent R3; Gill Instruments, Lymington, UK) to measure wind speed components (u, v, w) and an open-path analyser to measure CO₂ and H₂O (Model LI-7500; LI-Cor Inc., Lincoln, NE, USA). Since the start of flux measurements, data has been recorded at a frequency of 10 to 20 Hz by a computer (2003-2016) or data logger (2017-onwards, Model CR3000, Campbell Scientific Inc., Logan, UT, USA) (Allard et al., 2007; Klumpp et al., 2011). Towers in both experimental fields are part of international monitoring networks; the extensive treatment is part of the European FLUXNET network (station ID: FR-Lq2) and the intensive management treatment is part of the Integrated Carbon Observation System - ICOS (station ID: FR-Lqu). High frequency weather data (T<sub>air</sub>: air temperature, RH: relative humidity, PA: atmospheric pressure, PPT: total precipitation, ws: wind speed), solar radiation (PAR: photosynthesis active radiation, RG: global radiation) and information on soil moisture (0-20 cm) is provided by an onsite meteorological station. The footprint of each eddy covariance tower is non-overlapping and confined to a single management treatment, as indicated by wind rose analysis. In this study, the daily averages, minimum and maximum air temperature are hereafter referred to as T<sub>air</sub>, T<sub>min</sub> and T<sub>max</sub>, respectively. The frequency of atmospheric and solar radiation measurements is 30 s and 20 s respectively.

Raw-data processing of flux data for this study was carried out using EddyPro® software (Li-COR, version 7.0.9) following Winck et al. (2023). In brief, pre-processing included the calculation of half-hour turbulent fluxes from high frequency measurements and the averaging of meteorological variables for the corresponding time steps. The post-processing of half-hour data included data quality checks (Foken et al., 2004; Rebmann et al., 2005), uStar estimation and filtering (Papale et al., 2006), gap-filling (Falge et al., 2001; Pastorello et al., 2020), and C flux partitioning (Reichstein et al., 2005). Net Ecosystem Exchange (NEE) values were included within the range from -50 µmol s⁻¹ m⁻² to +30 µmol s⁻¹ m⁻², in line with Vitale et al. (2019). NEE values were rejected when flagged as low-quality fluxes (Foken et al., 2004;
Rebmann et al., 2005), above three standard deviations (Winck et al., 2023), recorded under insufficient atmospheric turbulence (Papale et al., 2006), and/or classed outside of the flux tower footprint (Kljun et al., 2004). We also rejected long runs of identical NEE as such patterns indicate instrumental measurement failures (Winck et al. 2023). Data cleaning and uStar filtering were carried out using both “FreddyPro” (https://github.com/cran/FREddyPro) and “REddyProc” packages (Wutzler et al., 2018). Values of NEE were imputed using Marginal Distribution Sampling-MDS as recommended by FLUXNET (Pastorello et al., 2020), using the R package “REddyProc” and then partitioned into gross primary productivity (GPP), a proxy for ecosystem photosynthesis, and ecosystem respiration ($R_{eco}$), a proxy for plant and soil respiration, using night-time algorithms (Reichstein et al., 2005). This approach minimizes the risk of overestimating $R_{eco}$ in cool, mesic systems with snow in the winter, and was hence better suited to our upland grassland system than alternative possible flux partitioning approaches for dry ecosystems (Lasslop et al. 2010, 2012).

For the present work, we only considered periods in which complete daily observations were available for both management treatments, that is, 48 half-hour observations. Patterns of NEE in the resulting dataset showed clear seasonal cycles, with highest CO₂ uptake rates during spring and summer (i.e., negative NEE), and comparably low CO₂ emission rates (i.e., positive NEE) during the winter months (NEE, Fig. S2). Daily flux values were obtained by summing the half-hour observations of GPP or $R_{eco}$, and the final dataset contained 4487 observations (out of a potential 6940 expected observations for the temporal series from 2003 to 2021).

## 2.3. Anomaly detection in air temperature and flux data

In line with IPCC methodology (Seneviratne et al., 2021), we consider a temperature (or CO₂ flux) event to be anomalous if the value of the variable exceeds (or lies below) a specified threshold in relation to the complete dataset. World Meteorological Organisation recommendations are that a 30-year time series should be used where possible for calculating climate averages (normals); for $T_{air}$, the anomalies were computed over a climatology period of 25 years (the longest climatic series available for the field site). For CO₂ fluxes, calculations were carried out using the available data from 2003 to 2021. $T_{air}$ and CO₂ fluxes were considered as anomalous when below the 10th percentile (lower than expected) or above the 90th percentile (higher than expected). Extreme events were defined when daily anomalies occurred in at least 6 consecutive days, and subsequently characterised by their duration (from the start date to the end date) and mean intensity (the distance of the observed value from the seasonal mean value, that is calculated by grouping the data by day-of-year, smoothing them with a rolling average of 5-day double-sided window). Henceforth, $T_{air}$ anomalies are referred to as cold (lower than expected, negative anomaly) and warm (higher than expected, positive anomaly), whereas CO₂ flux anomaly categories are referred to as -GPP and $-R_{eco}$ (lower than expected) and +GPP and $+R_{eco}$ (higher than expected).

Cold and warm $T_{air}$ anomalies were calculated from $T_{min}$ and $T_{max}$, respectively, and CO₂ flux anomalies from daily sum of GPP and $R_{eco}$. Given that $T_{air}$ and CO₂ fluxes exhibited seasonal trends, we first detrended the time series using a linear filter before identifying the anomalous events (Rogger et al., 2022; Zscheischler et al., 2013). The response variables were converted into a time series using the stats package (R software, R Core Team 2019), and fitted as linear models that included time and cycle as constants. Detrended time series were derived from the difference between the measured values and linear trends obtained from the fitted models. All these analyses were carried out using “HeatwaveR” package (Hobday et al., 2016; Schlegel and Smit, 2018).

## 2.4. Statistical analysis

All the data were statically analysed using R software (R Core Team, 2019). We used generalized additive mixed model to test for the interactive and individual effects of $T_{air}$ category (normal, warm, cold) and grassland management on CO₂ fluxes using the “gamm4” package (Pedersen et al., 2019). Months nested within $T_{air}$ category were inserted as smoothing term, and month nested in year as random intercept in our mixed models. The maximum complexity of the smoothing term (K) was verified using the effective degrees of freedom (EDF). Segmented linear models were used to evaluate “tipping-points” in $T_{air}$-CO₂ fluxes relationships for the two management treatments using the “segmented” package (Muggeo, 2008), determining segment numbers with respect to best R² and CI values. To determine whether CO₂ fluxes responded differently under anomalous and extreme $T_{air}$ conditions, we fitted linear models using the “lm” function from “Stat” package (Bolar, 2022); temperature categories were normal, warm, cold, extreme warm and extreme cold. Model selection procedures and variance partitioning were also used to explore the influence of soil moisture on flux-temperature relationships.

The probability of simultaneous events of $T_{air}$ and CO₂ fluxes anomalies were tested using $\chi^2$ methodology. The data were first organized in a contingency table (3 × 3 × 2), and the Cochran-Mantel-Haenszel test was used to infer the association between CO₂ flux category (lower-than-expected, normal, higher-than-expected) and $T_{air}$ category (cold, normal, warm), while controlling for grassland management (extensive, intensive). Within each grassland management, Pearson's chi-squared ($\chi^2$) was used to explore associations between CO₂ flux and $T_{air}$ categories in 3 × 3 contingency table. Finally, within each $T_{air}$ category, we organized a 3 × 2 contingency table to test the association between CO₂ flux category and grassland management. The $\chi^2$ P values were computed using 9,999 random permutations using the “wPerm” package (Weiss, 2022) and the strength of the associations were inferred using Cramer's V ($\phi_c$).

Pairwise comparisons in monthly fluxes between extensive and intensive grassland management were performed using Cohen's d effect-size (number of standard deviation units) and their confidence interval (95%) values was estimated using bootstrap methodology with 20,000 random permutations. These analyses were performed using the R package “bootES” (Kirby and Gerlanc, 2013). “Month” was inserted as the blocking column and “extensive” management as the control group; here the effect of intensive management is considered significantly positive or negative when intervals do not overlap zero (Nakagawa and Cuthill, 2007).

# 3. Results

## 3.1. Temperature anomaly patterns

Colder-than-average years were more common during the first half of the study period (2003-2013), whereas most years after 2013 were warmer than average (Fig. 1a). Despite this general tendency for warming over time, both warm and cold $T_{air}$ anomalies were observed throughout the study period (Fig. 1b) and in all seasons (Fig. S3). On average, 80 days per year were classed as colder (13 d to 69 d) or warmer (19 d to 87 d) compared to normal. Years with the highest number of warm temperature anomalies were 2003, 2015, 2019, and 2020, whereas cold anomalies were most frequent in 2007, 2010, 2013, and 2021 (Fig. 1b). Warm and cold anomalies lasted an average of 3 days (±0.1); during warm temperature anomalies, the daily $T_{air}$ was 7°C ± 0.3°C higher than normal, whereas $T_{air}$ was 6°C ± 0.7°C lower than the normal during cold temperature anomalies. In the case of extreme temperature events (duration >6 days), density distributions revealed that extreme cold events lasted an average of 7 d, whereas warm extreme events presented a second distribution peak at 11 d (Fig. 1c). The highest number of warm extremes were observed in 2003, 2015,
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Air temperature variation over the study period at the Laqueuille grassland site: (a) Deviation of air temperature ($T_{\text{air}}$), maximal temperature ($T_{\text{max}}$) and minimal temperature ($T_{\text{min}}$) from the long-term average (1996–2021); (b) Number of anomalous air temperature events per year; (c) Duration and frequency of extreme events ($\ge$ 6 days). Air temperature is considered as anomalous when below the 10th percentile (colder than expected) or above the 90th percentile (warmer than expected).</figcaption></figure>

2019, 2020 whereas the highest number of cold extremes were observed in 2007, 2010, 2013, 2017 (Fig. S4). Extreme cold events occurred most frequently during the winter (8 ± 1 events per year), autumn (7 ± 1 events) and spring (6 ± 1 events per year). Extreme warm events were more frequent during the summer (11 events), but also occurred in autumn (7 events). In general, the average temperature during extreme events was either 9°C ± 1°C lower or higher than normal.

## 3.2. CO₂ flux patterns under normal versus anomalous temperature conditions

After flux partitioning, mean monthly sums of GPP (gross primary productivity) and $R_{\text{eco}}$ (ecosystem respiration) ranged from $10 \text{ g m}^{-2} \text{ month}^{-1}$ to $213 \text{ g m}^{-2} \text{ month}^{-1}$ and $20.5 \text{ g m}^{-2} \text{ month}^{-1}$ to $190 \text{ g m}^{-2} \text{ month}^{-1}$ of C respectively in the two management treatments (Table S2). In general, GPP and $R_{\text{eco}}$ fluxes showed a similar magnitude of response to warm anomalies (+16 % on average during the year), whereas $R_{\text{eco}}$ showed a greater magnitude of change in response to cold anomalies compared with GPP (decreases of 28 % versus 18 %). However, temperature anomalies induced a shift in timing and/or magnitude of the peak of monthly GPP and $R_{\text{eco}}$ values (Fig. 2), with effects varying depending on management treatment (Table S3). In the extensive management treatment, warm anomalies were associated with a significant increase in peak GPP and $R_{\text{eco}}$ values during May-June without a significant change in timing (+ 21 % and 26 % for GPP and $R_{\text{eco}}$ respectively). In contrast, cold anomalies were associated with both a decrease in peak GPP and $R_{\text{eco}}$ values (- 18 % and 28 % for GPP and $R_{\text{eco}}$ respectively), and a shift towards later peak flux values (Fig. 2a-d). In the intensive management treatment, warm temperature anomalies were associated with both earlier and greater peak CO₂ fluxes compared to normal temperature conditions (+ 21 % and 26 % for GPP and $R_{\text{eco}}$ respectively). However, in the summer period, CO₂ fluxes were lower under warm temperature anomalies compared to those under normal temperatures (Fig. 2b, d). In addition, cold temperature anomalies were only associated with delayed peaks in GPP and $R_{\text{eco}}$ values in the intensive management treatment, and the magnitude of peak CO₂ fluxes was unchanged (Fig. 2a-d).

Observed differences in CO₂ flux patterns under different temperature categories generated temporal variation in treatment differences (Fig. 2e-j). Under normal temperature conditions, peak CO₂ fluxes occurred during the same period for both treatments (May and June for GPP and $R_{\text{eco}}$ respectively), but values of GPP and $R_{\text{eco}}$ were generally higher in the intensive compared to the extensive management treatment (Fig. 2f, i). Under cold anomalies, CO₂ fluxes showed increased

divergence between treatments during the growing season, with the greatest difference in August (~ 34 % and 29 % for GPP and $R_{\text{eco}}$ respectively) (Fig. 2 e,h). Under warm temperature anomalies, divergence in flux values between management treatments was limited to the later part of the growing season, with the largest differences occurring in September for GPP (~30 %) and October for $R_{\text{eco}}$ (~ 27 %) (Fig. 2 g,j).

## 3.3. Relationships between CO₂ fluxes and temperature under temperature anomalies

Irrespective of management treatment, daily GPP and $R_{\text{eco}}$ showed positive relationships with $T_{\text{air}}$ up to a threshold of 20°C (Fig. 3). All CO₂ fluxes levelled off or showed a negative relationship with $T_{\text{air}}$ at air temperatures above 20°C. Moreover, soil moisture had very little effect on fluxes when $T_{\text{air}}$ was below 20°, but it had a significant positive effect on fluxes at air temperatures above 20°C (Fig. S5). Effects of air temperature and soil moisture were additive in both management treatments (Fig. S5).

Sensitivity of CO₂ fluxes to temperature varied depending on $T_{\text{air}}$, with relatively stronger temperature responses at intermediate temperatures (5°C to 10°C) compared to temperatures below 5°C or between 10°C and 20°C (Fig. 3). Furthermore, the degree to which CO₂ fluxes responded to temperature at the extremities of the temperature gradient (i.e. < 5°C, > 20°C) varied depending on management (Table S4). Both GPP and $R_{\text{eco}}$ showed greater temperature-induced increases in the intensive compared to the extensive management treatment under relatively cold conditions (< 5°C threshold). In contrast, above 20°C, temperature-induced decreases in GPP and $R_{\text{eco}}$ were greater in the intensive compared to the extensive management treatment (Fig. 3). Interestingly, plant-driven evapotranspiration (ET) was higher in the intensive compared to extensive management treatment, but treatment-induced differences varied depending on temperature; below 20°C, ET was 21 % higher in the intensive compared to the extensive treatment whereas above 20°C, ET was only 8 % higher in the intensive treatment (data not shown).

Slopes of relationships between fluxes and temperature (i.e. process rates) varied depending on $T_{\text{air}}$ category (extreme cold, cold, normal, warm, extreme warm), time of year (seasonal scale) and management treatment (Fig. 4). In general, temperature sensitivity (i.e. slope size) of both GPP and $R_{\text{eco}}$ was high in spring and autumn but limited in winter. Extreme warm events were associated with negative temperature responses for GPP during summer and autumn but did not have significant effects in spring in either management treatment (Fig. 4). Positive temperature-GPP relationships in autumn could be ranked in terms of
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Effects of air temperature category and grassland management on mean daily CO₂ fluxes per month (R<sub>eco</sub>, ecosystem respiration; GPP, gross primary production). Response curves are derived from a generalized additive mixed model. Air temperature categories are defined as cold (below the 10th percentile) or warm (above the 90th percentile) with respect to long-term averages. See also Table S2 and S3.</figcaption></figure>

sensitivity as follows: extreme cold > cold > warm > normal. In spring, the ranking of temperature sensitivity of positive temperature-GPP relationships could be ranked: warm > normal > cold/extreme cold.

In the case of R<sub>eco</sub>, extreme warm events were associated with negative temperature responses during summer and autumn across management treatments, whereas positive flux-temperature relationships were observed in spring under extensive management conditions alone (Fig. 4). Slopes of positive temperature/R<sub>eco</sub> relationships showed similar ranking to that of GPP in relation to T<sub>air</sub> categories in spring. However, slope values of the T<sub>air</sub>-R<sub>eco</sub> relationships were generally lower in spring compared with those observed for the T<sub>air</sub>-GPP relationships.

Interactions between management treatment and T<sub>air</sub> occurred in less than half of the “temperature-season” combinations; in most cases, significant relationships between fluxes and T<sub>air</sub> had significantly larger values for slopes in the intensive compared to the extensive management treatment. The only exceptions were for GPP and R<sub>eco</sub> in spring, under extreme warm conditions, and in summer under cold conditions where

the extensive management treatment showed greater sensitivity to T<sub>air</sub> (Fig. 4).

### 3.4. Patterns of CO₂ flux anomalies in extensive and intensive management treatments

Days with fluxes reported as anomalous accounted for approximately 30 % of our dataset, irrespective of management treatment (Table S5). Of these days, 77 % occurred under normal temperature conditions. When controlling the influence of grassland management, we found a significant association between the occurrence of flux anomalies and air temperature categories (Table 1). “Higher-than-expected” flux anomalies occurred more frequently than “lower-than-expected” flux anomalies under warm temperature anomalies, but the reverse pattern was observed under cold temperature anomalies. Management treatment influenced the association between R<sub>eco</sub> anomalies and temperature anomalies but had no effect on the association between GPP anomalies
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Effects of grassland management (intensive, extensive) on relationships between CO₂ fluxes (R<sub>eco</sub>, ecosystem respiration; GPP, gross primary production) and air temperature. Results of segmented regressions with the “tipping points” and slopes (β) of regression segments (S) are shown. See also Table S4.</figcaption></figure>

and temperature (Table 1). In warmer-than-normal temperatures there were relatively more negative R<sub>eco</sub> anomalies in the intensive compared to the extensive treatment, but more occurrences of positive R<sub>eco</sub> anomalies in the extensive compared to the intensive treatment.

Flux anomalies varied in magnitude depending on flux anomaly category, time of year and management treatment (Fig. 5). Average monthly fluxes recorded during anomalous GPP and R<sub>eco</sub> flux events were generally higher in the intensive management treatment compared to the extensive management treatment (Fig. 5). Divergence in GPP anomalies between management treatments was particularly pronounced in spring and summer for higher-than-expected fluxes and in August for lower-than-expected fluxes (Fig. S6). Generally, +GPP anomalies showed more treatment differences compared with -GPP anomalies. In contrast, +R<sub>eco</sub> anomalies showed greater treatment differences compared with -R<sub>eco</sub> anomalies in late summer and autumn alone (Figs. 5 & S6). In the case of extreme CO₂ fluxes (i.e. anomalous fluxes >6 consecutive days), the magnitude of fluxes was significantly higher in the intensive treatment compared to the extensive treatment (x 1.3 on average, Fig. 6).

Overall, “higher-than-expected” fluxes had a greater contribution to monthly fluxes compared with “lower-than-expected” fluxes for both GPP (27 %) and R<sub>eco</sub> (24 %) (Fig. 7a-d, Table S2). The contributions of both categories of flux anomalies to overall fluxes were greater than 40 % during the winter months of the year, but lower during the growing season (Fig. 7e-f). Although the relative contribution of flux anomalies varied over the course of the year in both management treatments, the total contribution of flux anomalies during the year did not differ between management treatments. Average contribution of flux anomalies to overall fluxes was 39 % during non-growing season and 34 % during growing season.

## 4. Discussion

The unprecedented temperature extremes recorded in recent years

call for urgent field-based research into ecosystem responses to temperature anomalies and associated changes in the C sink capacity of ecosystems (Pan et al., 2020). Our analysis of grassland CO₂ flux data from a long-term upland grassland management trial shows that temperature anomalies affect the magnitude of CO₂ fluxes, the relationship between air temperature (T<sub>air</sub>) and CO₂ fluxes, and the occurrence of anomalous flux events. Our study highlights three key findings in relation to the context-dependency of grassland responses to temperature anomalies: (1) CO₂ fluxes show greatest sensitivity to temperature anomalies at the start of the growing season; (2) warm temperature anomalies have greater impacts on T<sub>air</sub>-CO₂ flux relationships compared to cold anomalies, but effects vary depending on season; (3) high grassland management intensity promotes sensitivity of CO₂ fluxes to warm temperature anomalies in this study system. Our results bring new insights into the patterns of flux anomalies under contrasting grassland management treatments and underscore the importance of management x climate interactions for the grassland C balance in cool temperate systems.

Numerous studies have suggested that grassland sensitivity to climatic anomalies may depend on the nature and timing of the climatic event (Chen et al., 2023; Craine et al., 2012; De Boeck et al., 2018). In line with our first hypothesis, we found that CO₂ flux responses to temperature anomalies showed seasonal variation; irrespective of management treatment, flux responses to both warm and cold temperature anomalies were greater during the spring and up to the peak of the growing season (June). Warm temperature anomalies increased CO₂ fluxes during this period whereas cold temperature anomalies decreased CO₂ fluxes up to the peak of the growing season. The greater degree of change in CO₂ fluxes observed in spring agrees with the higher plant sensitivity to warming documented during the exponential growth phase of herbaceous plants (De Boeck et al., 2011). Both our extensively- and intensively-managed grasslands showed significant increases in peak monthly GPP values in response to warm anomalies, suggesting that warm temperature anomalies drive significant gains in spring
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Temperature sensitivity of daily CO₂ fluxes in relation to grassland management, season, and air temperature category, as assessed by the slopes between air temperature and either gross primary production (GPP) or ecosystem respiration (R<sub>eco</sub>). Significant interactions between treatment and air temperature are indicated with an asterisk. Unfilled bars indicate no significant relationship between temperature and CO₂ fluxes for a given treatment. The summary of linear models is presented in Table S6. † indicates few numbers of observations, ‡ indicates missing values of GPP and Reco for such temperature category. See also Table S5.</figcaption></figure>

fodder production in this humid upland environment. These results confirm the temperature-sensitivity of ecosystems in colder environments (Ernakovich et al., 2014; Wang et al., 2021), and are consistent with remote-sensing data showing positive effects of heatwaves on canopy greening in montane grasslands in the absence of water deficit (Corona-Lozada et al., 2019).

Variation in flux responses to temperature anomalies during the growing season may at least partly be linked to flux-temperature relationships (Li et al., 2023). Overall, our two managed grasslands showed typical bell-shaped GPP-temperature relationships, and an optimal air temperature of 20°C for daily fluxes, in line with T<sub>air</sub> optima for GPP in grassland systems reported elsewhere (Bennett et al., 2021; Duffy et al., 2021; Ping et al., 2023). However, sensitivity of C fluxes to T<sub>air</sub> varied depending on season and the nature of temperature anomalies, with greatest responses to warm rather than cold anomalies. Warm temperature anomalies upregulated T<sub>air</sub>-CO₂ flux relationships in spring but down-regulated these relationships in summer; extreme warm events also down-regulated CO₂ flux sensitivity to temperature in autumn. These results likely reflect higher levels of photorespiration at high temperatures (Rogger et al., 2022) and increased probability of exceeding heat stress thresholds (Wahid et al., 2016), as well as potential confounding effects of plant developmental stage, soil water availability

and light radiation (Wingler and Hennessy, 2016). When soil moisture is not limiting as typically in spring/ early summer, higher temperatures associated with sunny conditions lead to higher photosynthetic rates. Under heatwave conditions (T<sub>air</sub> > 20°C) however, we found that variation in fluxes was driven by soil moisture sensitivity rather than temperature, irrespective of management treatment. Unlike warm anomalies, T<sub>air</sub>-CO₂ flux relationships were similar under cold anomalies compared with normal temperature conditions, possibly linked to the capacity of temperate grasses to acclimate to colder conditions (Körner, 2016; Wingler and Hennessy, 2016). Although high altitude (alpine) grasslands are considered to be particularly sensitive to warming and temperature anomalies (Brilli et al., 2023), our findings suggest that responses of mid-altitude grassland systems to warm temperature anomalies should not be overlooked.

Given that plant and soil properties show consistent patterns of variation along management intensity gradients (Bloor et al., 2021; De Keersmaecker et al., 2015; Grigulis et al., 2013), we expected to find interactions between management treatments and temperature anomalies on CO₂ fluxes in this study. Although the directionality of flux responses to temperature anomalies at the start of the growing season was the same across management treatments, differences in the magnitude of response to temperature anomalies generated shifts in the relative
<caption>**Table 1**</caption>

Associations between occurrence of normal or anomalous CO₂ fluxes (R<sub>eco</sub>, ecosystem respiration; GPP, gross primary production) and temperature under contrasting grassland management treatments. Air temperature categories are defined as cold (below the 10th percentile) or warm (above the 90th percentile) with respect to long-term averages. CO₂ flux categories are defined as lower-than-expected (-), normal, higher-than-expected (+); numbers of days per flux category are given.

<table><thead><tr><th rowspan="2">Temperature category</th><th colspan="4">Carbon flux category</th><th rowspan="2">χ<sup>2</sup> †</th></tr><tr><th>Management</th><th>-GPP</th><th>GPP</th><th>+GPP</th></tr></thead><tbody><tr><td rowspan="2">Cold</td><td>Extensive</td><td>101</td><td>397</td><td>39</td><td rowspan="2">0.73</td></tr><tr><td>Intensive</td><td>111</td><td>391</td><td>35</td></tr><tr><td rowspan="2">Normal</td><td>Extensive</td><td>546</td><td>2384</td><td>556</td><td rowspan="2">0.44</td></tr><tr><td>Intensive</td><td>526</td><td>2398</td><td>562</td></tr><tr><td rowspan="2">Warm</td><td>Extensive</td><td>45</td><td>296</td><td>123</td><td rowspan="2">4.9</td></tr><tr><td>Intensive</td><td>64</td><td>296</td><td>104</td></tr><tr><td>Flux x temperature, per management treatment</td><td colspan="5">Extensive: χ<sup>2</sup>=75.437, df=4, Cramer's V=0.092, P-value &lt;0.001,<br/>Intensive: χ<sup>2</sup>=56.083, df=4, Cramer's V=0.079, P-value &lt;0.001,</td></tr><tr><td>Flux x temperature, controlling for management</td><td colspan="5">χ<sup>2</sup>=125.77, df=4, Cramer's V=0.084, P-value &lt;0.001</td></tr></tbody></table><table><thead><tr><th>Temperature category</th><th>Management</th><th>-R<sub>eco</sub></th><th>R<sub>eco</sub></th><th>+R<sub>eco</sub></th><th>χ<sup>2</sup> †</th></tr></thead><tbody><tr><td rowspan="2">Cold</td><td>Extensive</td><td>135</td><td>387</td><td>15</td><td rowspan="2">0.53</td></tr><tr><td>Intensive</td><td>131</td><td>387</td><td>19</td></tr><tr><td rowspan="2">Normal</td><td>Extensive</td><td>541</td><td>2435</td><td>510</td><td rowspan="2">2.52</td></tr><tr><td>Intensive</td><td>501</td><td>2443</td><td>542</td></tr><tr><td rowspan="2">Warm</td><td>Extensive</td><td>29</td><td>281</td><td>154</td><td rowspan="2">11.74</td></tr><tr><td>Intensive</td><td>58</td><td>276</td><td>130</td></tr><tr><td>Flux x temperature, per management treatment</td><td colspan="5">Extensive: χ<sup>2</sup>=217.347, df=4, Cramer's V=0.156, P-value &lt;0.001,<br/>Intensive: χ<sup>2</sup>=137.620, df=4, Cramer's V=0.124, P-value &lt;0.001</td></tr><tr><td>Flux x temperature, controlling for management</td><td colspan="5">χ<sup>2</sup>=342.34, df=4, Cramer's V=0.14, P-value &lt;0.001</td></tr></tbody></table>

performance of our two grasslands over time. In the intensive management treatment, peak flux values in both GPP and R<sub>eco</sub> were delayed rather than depressed under cold anomalies, possibly indicating compensatory plant growth later in the growing season (Li et al., 2021). Management treatment differences in fluxes under normal temperatures (e.g., intensive > extensive) were exacerbated during the growing season under cold temperature anomalies. Such interactive effects of management and temperature anomalies on CO₂ fluxes may be driven by differential rates of biomass production, biomass removal and labile C inputs to the soil which affect soil and microbial respiration, as well as changes in soil temperature due to different disturbance levels in the different grazing regimes (Barneze et al., 2022). Our results point to a greater capacity of intensive grasslands to maintain their function in terms of CO₂ fluxes when cold temperature anomalies occur during the summer period; these anomalies rarely push plants beyond cold stress thresholds at this time and may concurrently alleviate levels of water stress (Li et al., 2024). Higher GPP in late summer under cold anomalies may be promoted by the greater growth potential of plants in the vegetative stage which are relatively more abundant in intensively-compared to extensively-managed grasslands (Rossignol et al., 2011), as well as by inherent differences in soil fertility and dominant species between the two management treatments.

Unlike cold temperature anomalies, warm temperature anomalies promoted convergence in CO₂ flux values between the intensive and extensive management treatments during the growing season. This result was driven by greater temperature-induced decreases in GPP and R<sub>eco</sub> in the intensive rather than extensive management treatment under warm anomalies; fluxes in the extensive management treatment were maintained at levels close to those observed under normal temperature

conditions. This pattern of response may at least partly reflect treatment differences in evapotranspiration; higher evapotranspiration recorded in the intensive treatment may have led to earlier soil water depletion and stronger decreases in GPP at higher temperatures compared to the extensively-managed treatment. Warm temperature anomalies therefore appear to generate greater impacts on ecosystem function in our intensive rather than extensive grassland system, as previously observed for grassland biomass production in other studies (Grigulis and Lavorel, 2020). The higher temperature sensitivity observed here for the intensive grassland treatment supports the idea that low grassland management intensity may buffer CO₂ fluxes against warm air temperature anomalies and opens avenues for optimizing management practices to maintain grassland soil C stocks and productivity under changing environmental conditions. Given that the warm temperature anomalies recorded in the present study (+7°C on average) correspond to significantly greater levels of warming than the majority of controlled experiments to date (see recent metanalyses by Shi et al., 2022; N. Wang et al., 2019), we also encourage Next Generation Climate Change experiments to integrate higher levels of warming in their experimental designs to better represent climatic extremes.

As far as we are aware, no previous field study has simultaneously addressed grassland CO₂ flux responses to both cold and warm temperature anomalies. Any differences in sensitivity of GPP and R<sub>eco</sub> to temperature anomalies may promote divergence in the temporal dynamics of GPP and R<sub>eco</sub>, with implications for the net C balance over the course of the year (Duffy et al., 2021). On average over the year, and irrespective of management treatment, we found that cold rather than warm temperature anomalies promoted asynchrony between C uptake and loss since GPP and R<sub>eco</sub> fluxes showed a similar magnitude of increase to warm anomalies whereas R<sub>eco</sub> showed a greater decrease in response to cold anomalies compared with GPP. At the annual scale, our study therefore suggests that cold anomalies may enhance the C sink capacity of this upland grassland system; any decrease in the probability of occurrence of cold temperature anomalies as a cause of ongoing climate change could thus represent a risk of climate change-induced destabilization of large soil C stocks for this system (Wang et al., 2019). Our findings for warm temperature anomalies > 7°C contrast with the greater sensitivity reported for R<sub>eco</sub> compared to GPP in temperate grasslands in a review of warming experiments simulating <4°C increases (Wang et al., 2019) and may reflect transient changes in the allocation of fixed C between biomass and soil (Fernández-Martínez et al., 2014). However, it is also possible that relatively high degree of GPP sensitivity to warm anomalies recorded here may have been influenced by our flux partitioning approach based on night-time algorithms alone (Reichstein et al., 2005). In particular, overestimations of both GPP and temperature sensitivity of GPP could potentially arise in the drier, latter half of the growing season (Tramontana et al., 2020). Although current flux processing recommendations are to use a single approach to ensure consistency, partitioning of NEE for daytime and nighttime data better accounts for stomatal closure and reduces the risk of overestimating R<sub>eco</sub> under dry conditions. The influence of flux partitioning method during the summer could be further explored, as in recent studies on forest ecosystems (Perez-Quezada et al., 2018; Kantenberg et al., 2020). Functional responses to climate anomalies may range from little or no response to responses that are large or 'extreme' (Smith, 2011). Irrespective of management treatment, we found that most days with anomalous fluxes occurred under 'normal' temperature conditions. This agrees with outputs of modelling work on global C fluxes, where less than 30% of C flux anomalies in Europe were found to occur in concert with temperature extremes (Pan et al., 2020). Whilst temperature has previously been shown to be the primary driver of flux variation in our upland grassland system (Winck et al., 2023), it seems reasonable to assume that low soil water and/or high evaporative demand due to windy conditions may promote the occurrence of anomalous flux events before thresholds of temperature anomalies are reached. Temperature anomalies may also initiate lagged extreme responses in C

<footnote>† Significant Chi-squared results comparing treatments in each temperature category are given in bold.</footnote>
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Effects of grassland management on mean daily values of anomalous CO₂ fluxes recorded per month over the study period. Gross primary productivity (GPP) and ecosystem respiration ($R_{eco}$) are classed as lower than expected (-) when below the 10th percentile or higher than expected (+) when above the 90th percentile. Unfilled box-plots correspond to non-significant differences between management treatments for a given month.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Influence of management (Extensive, intensive) on the monthly average of CO₂ flux anomaly intensity. Gross primary productivity (GPP) and ecosystem respiration ($R_{eco}$) are classed as lower than expected (-) when below the 10th percentile, or higher than expected (+) when above the 90th percentile. Treatment differences are indicated by Cohen's *d* values and are significant where confidence intervals do not overlap zero (the extensive treatment is considered the control). Number of observations for each management combination is indicated (n).</figcaption></figure>

fluxes or legacy effects, especially in the case of thermal damage (Qu et al., 2024; Reichstein et al., 2013). Simulated flux responses to climate anomalies suggest that compound events are likely to promote the occurrence of flux anomalies at the annual level (Luo et al., 2008; Pan et al., 2020). However, the incidence of anomalous C flux events may be difficult to predict based on climatic data alone due to interactions with management events and plant phenology (Rogger et al., 2022). Indeed, our data showed that management treatment modified the association between incidence of temperature and $R_{eco}$ anomalies; warm temperature anomalies promoted the occurrence of negative $R_{eco}$ anomalies in the intensive treatment, but positive $R_{eco}$ anomalies in the extensive

treatment. Interactions between management and both the frequency and magnitude of flux anomalies in response to climate anomalies (as demonstrated in the present work) add to the complexity and uncertainty associated with modelling ecosystem responses in a changing environment.

Previous studies have suggested that flux anomalies could have disproportionate effects on the annual C balance of ecosystems (Kannenberg et al., 2020; Jakob Zscheischler et al., 2014). In our study, anomalous flux events (i.e. 30 % of days in our dataset) accounted for approximately one third of the total monthly fluxes recorded for GPP and $R_{eco}$ irrespective of management treatment. In the case of positive
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Effects of grassland management on the absolute contribution of anomalous fluxes to the monthly sum of CO₂ fluxes presented in absolute terms (a-d), and in relative terms (e-f). Gross primary productivity (GPP) and ecosystem respiration ($R_{eco}$) are classed as lower than expected (-) when below the 10th percentile or higher than expected (+) when above the 90th percentile. Dashed lines indicate the start and the end of the growing season.</figcaption></figure>

flux anomalies (i.e. higher than expected fluxes), the magnitude of these fluxes was consistently higher in the intensive management treatment compared to the extensively-managed grassland in the latter half of the growing season. This response pattern is likely linked to fertiliser application in the intensive grassland since increased N availability for plant growth and microbial activity is known to stimulate ecosystem C fluxes (Rumpel et al., 2015), in line with the idea that grassland management events have the potential to induce C flux anomalies (Rogger et al., 2022). Nevertheless, the overall contribution of flux anomalies to monthly fluxes did not differ between management treatments at the annual scale and the contribution of anomalous fluxes to monthly fluxes was relatively constant throughout the year; to a large extent, “lower-than-expected” fluxes were compensated by “higher-than-expected” fluxes for both $R_{eco}$ and GPP. Hence, our results do not appear to support the idea that days with anomalous fluxes exert an outsized influence on GPP and $R_{eco}$ budgets. Of course, it is important to note that our dataset is a subset of the entire experimental period to ensure robust comparisons across management treatments, and it should not be extrapolated to generate annual budgets. Further work is required to better understand the drivers of C flux anomalies in managed grasslands, and to derive operational indicators of potential extreme flux responses to global change.

Taken together, our results show that grassland management interacts with the timing and nature of temperature anomalies on CO₂ fluxes, providing partial support for the hypothesis that low grassland management intensity buffers CO₂ fluxes against air temperature anomalies. To our knowledge, this is the first long-term field study to explicitly investigate the signature of temperature anomalies on grassland CO₂ fluxes under multiple management regimes, providing “real world” observations that complement existing short-term, controlled

mechanistic experiments. Differential sensitivities of grassland types to different temperature anomalies can offer opportunities for farmer adaptation to climate variability where different combinations of grassland types within the farm allow resource adjustments and farm-level stability in an uncertain climate (Plantureux et al., 2022). Our analytical framework using eddy-covariance flux data to explore temperature anomalies should be extended to include compound events and more grassland sites to increase the genericity of results.

## Data availability

The data that support the findings of this study are available on Dataverse INRAE at https://doi.org/10.57745/CBHNA8 upon request.

## CRediT authorship contribution statement

Bruna Winck: Writing - review & editing, Writing - original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Katja Klumpp: Writing - review & editing, Supervision, Investigation. Juliette M.G. Bloor: Writing - review & editing, Writing - original draft, Supervision, Methodology, Conceptualization.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Bruna Winck reports financial support was provided by French National Research Agency. If there are other authors, they declare that they have no known competing financial interests or personal relationships
that could have appeared to influence the work reported in this paper.

## Acknowledgements

CO₂ flux data was provided by national and European infrastructures of long-term experiments, ANAEE-FR and ICOS, co-financed by the French National Agency for Research (ANR-11-INBS-0001). Bruna Winck received a postdoctoral fellowship from the Auvergne-Rhône-Alpes region, through the CPER project "SERVICES" coordinated by Catherine Picon-Cochard. Climate monitoring data is from the INRAE CLIMATIK platform (https://agroclim.inrae.fr/climatik/, in French) managed by the AgroClim laboratory of Avignon, France. The authors thank D. Colosse, O. Darsonville and L. Michaud for technical support for FR-Laq and FR-Lqu site. We also thank T. Bremm and V. D. Pillar for support with data quality checks and statistical analysis validation, respectively.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.agrformet.2024.110343.

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