# Legacy Effects of the Siberian Heatwave of 2020 on Above- and Belowground Processes

Min Jung Kwon, Philippe Ciais, Ana Bastos, Christian Beer

► To cite this version:

Min Jung Kwon, Philippe Ciais, Ana Bastos, Christian Beer. Legacy Effects of the Siberian Heat-wave of 2020 on Above- and Belowground Processes. Global Biogeochemical Cycles, 2025, 39 (7), 10.1029/2025gb008607. [https://doi.org/10.1029/2025gb008607](https://doi.org/10.1029/2025gb008607). [hal-05172736](https://HAL.inistre.fr/hal-05172736v1)

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# Global Biogeochemical Cycles®

## RESEARCH ARTICLE

10.1029/2025GB008607

**Key Points:**

* In 2020, Siberia experienced an intense heatwave (HW) that affected the seasonality of CO₂ fluxes

* The effects of this 2020 Siberian HW persisted into 2021, leading to warmer and wetter soils, increased CO₂ emissions, and decreased soil carbon pools

* The effects of strong HWs can last for more than a year through both above- and below-ground processes

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

M. J. Kwon,
minjung.kwon86@gmail.com

**Citation:**

Kwon, M. J., Ciais, P., Bastos, A., & Beer, C. (2025). Legacy effects of the Siberian heatwave of 2020 on above- and belowground processes. *Global Biogeochemical Cycles*, *39*, e2025GB008607. https://doi.org/10.1029/2025GB008607

Received 7 APR 2025

Accepted 2 JUL 2025

**Author Contributions:**

**Conceptualization:** Min Jung Kwon, Philippe Ciais

**Formal analysis:** Min Jung Kwon

**Funding acquisition:** Philippe Ciais

**Investigation:** Min Jung Kwon

**Supervision:** Philippe Ciais, Christian Beer

**Visualization:** Min Jung Kwon

**Writing – original draft:** Min Jung Kwon

**Writing – review & editing:** Min

Jung Kwon, Philippe Ciais, Ana Bastos, Christian Beer

© 2025. The Author(s).

This is an open access article under the
terms of the Creative Commons

Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.

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## Legacy Effects of the Siberian Heatwave of 2020 on Above- and Belowground Processes

Min Jung Kwon¹,² ID, Philippe Ciais² ID, Ana Bastos³,⁴ ID, and Christian Beer¹,⁵ ID

¹Department of Earth System Sciences, Universität Hamburg, Hamburg, Germany, ²Laboratoire des Sciences du Climat et de l'Environnement (LSCE), CEA-CNRS-UVSQ, UMR8212, Gif-sur-Yvette, France, ³Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany, ⁴Leipzig University, Institute for Earth System Science and Remote Sensing, Leipzig, Germany, ⁵Center for Earth System Research and Sustainability, Universität Hamburg, Hamburg, Germany

**Abstract** In the first half of 2020, Siberia experienced an intense and persistent heatwave (HW). This HW induced earlier and stronger CO₂ uptake in the early growing season compared to previous years, which then led to reduced CO₂ uptake in the late growing season partly due to soil desiccation, that is, seasonal legacy effects. Using the land surface model ORCHIDEE-MICT, we investigated whether the 2020 HW influenced the ecosystem processes in the following year 2021 (legacy effects) by the differences between simulation results with the observed climate forcing in 2020 and counterfactual simulations without a prominent HW event in 2020. The 2020 HW, combined with increased snowfall in late 2020, resulted in a 1.2°C warmer soil temperature, 20 kg m⁻² more soil water content (in the top 1 m), and a 0.04 gC m⁻² day⁻¹ increase in heterotrophic respiration (CO₂ emissions) in early 2021 in central Siberia. The 2020 HW also affected photosynthesis (CO₂ uptake) in early summer 2021, with contrasting responses between biomes: CO₂ uptake increased in forests (i.e., positive legacy effects), while it decreased in grasslands (i.e., small negative legacy effects). Due to the 2020 HW, forests gained 6 gC m⁻² on land (combined plant and soil carbon), while grasslands lost 10.9 gC m⁻². Our results highlight that the effects of HWs can persist aboveground (vegetation) and belowground (soil temperature and moisture) and significantly affect carbon pools and net CO₂ fluxes in the following year.

## 1. Introduction

Heatwaves (HWs) have become more intense and frequent due to human-induced climate change (Perkins-Kirkpatrick & Lewis, 2020; Rousi et al., 2022). An extreme and persistent HW hit Siberia during January to May in 2020 (Ciavarella et al., 2021; Collow et al., 2022; Overland & Wang, 2021), causing earlier snowmelt and greater CO₂ uptake (Gloege et al., 2022; Kwon et al., 2021; Park et al., 2025). However, this increased photosynthetic activity in the early growing season depleted soil moisture and potentially nutrients in the late growing season, leading to early senescence (Kwon et al., 2021); such so-called seasonal legacy effects—enhanced spring photosynthesis suppressing late growing season photosynthesis—have been widely observed in the Northern Hemisphere (Bastos et al., 2020; Buermann et al., 2018; Lian et al., 2020; K. Wang et al., 2022; Wolf et al., 2016). Due to the seasonal compensation, the net annual CO₂ flux in 2020 was similar to that of previous years (Kwon et al., 2021).

However, the effects of HWs can also last beyond the year of occurrence (Qu, Chen, et al., 2024). Energy is the primary limiting factor for carbon (C) fluxes at high latitudes (Sonia I. Seneviratne et al., 2010), and strong and prolonged HW in the Arctic can increase energy input to the ecosystem and warm the soils, which can persist in subsequent months or years unless colder temperatures and/or less snowfall than usual cool the soils during the non-growing season. Warmer soil temperature anomalies in deeper layers may persist longer than in the shallow soils due to the delayed response at depth (Biskaborn et al., 2019). In addition, the effects of reduced soil water content driven by a HW and increased evapotranspiration can persist until precipitation replenishes the soil water storage (Lal et al., 2023; Suzuki et al., 2018). Heatwaves can also alter the vulnerability or resistance of vegetation (Descals, Verger, et al., 2022), and increase plant damage and/or mortality (Marchin et al., 2022; Margalef-Marrase et al., 2020; Ruehr et al., 2019), which can potentially reduce the C sink in the subsequent years. In addition, HWs increase the frequency and intensity of fires (Descals, Gaveau, et al., 2022; Pausas, 2022; Talucci et al., 2022), even in the following season or year (Forkel et al., 2012; Scholten et al., 2021), and consequently
modify above- and below-ground biomass and soil C for a longer period of time (Sippel et al., 2018; Trumbore et al., 2015). These changes in the soil environment and vegetation can modify CO₂ fluxes for more than a season or a year, as ecosystems may take several years to recover from disturbances (Anderegg et al., 2015; Saatchi et al., 2013; Schwalm et al., 2017; Wigneron et al., 2020; Yang et al., 2018).

Because climate extremes do not limit their legacy effects on a seasonal scale, we revisit the Siberian 2020 HW to examine its legacy effects through the end of 2021. There are several methods that can be used to estimate the legacy effects of HW. One option is to conduct a field manipulation experiment with control and warming plots. After a certain period of warming treatment in the warming plots, the same ambient climate can be applied to both treatment plots. The legacy effects could then be estimated by calculating the differences between the observations of the two treatments. However, such experiments are very expensive, especially when including several different ecosystems and large trees in boreal forests, and require several years to set up and collect data. Alternatively, machine learning techniques can be used to examine observational data, including warming events, and thus estimate legacy effects from the differences between predicted and the actual values, as in the study by Bastos et al. (2021) and Yu et al. (2022). However, these techniques do not represent the underlying processes and therefore have a limited explanatory power. Process-based land surface models (LSMs) are efficient and important tools to estimate the legacy effects of extreme climate events on large spatial and temporal scales, because they are calibrated using in situ observations and include critical and diverse ecosystem processes coupled to the atmosphere. LSMs simulate coupled physical, hydrological, and biogeochemical processes based on the past ecosystem states and current climates. Spatiotemporal variations in climate, as well as coupling among drivers that differ by plant functional type (PFT) or biome due to differences in C and water use efficiency (El Masri et al., 2019; Qu, Dong, et al., 2024; Qiao et al., 2019; Tang et al., 2014; Zhang et al., 2014), can lead to a wide range of ecosystem responses. Thus, this LSM simulation experiment is a sophisticated tool for estimating the climate legacy effects over large spatial extents, which cannot be easily or accurately performed by other methods. Bastos et al. (2020) employed this experimental design and used an ensemble of multiple LSMs to estimate the seasonal legacy effects of the 2018 European HW. In this study, we use the LSM ORCHIDEE and focus on (a) the legacy effects in soil temperature and soil moisture, and (b) assess whether these effects influence R<sub>h</sub> and NPP, and consequently, the net ecosystem CO<sub>2</sub> exchange (NEE; R<sub>h</sub> – NPP), and soil C and plant biomass. In addition, (c) we compare the responses of forests and grasslands, and examine whether their legacy effects differ.

## 2. Methods

We extended the land surface model simulation from 1979 to 2020 in Kwon et al. (2021) up to the end of 2021. To summarize the simulation setups, we used ORCHIDEE-MICT (Organizing Carbon and Hydrology in Dynamic Ecosystems-aMeliorated Interactions between Carbon and Temperature; v8.7.1) for a land surface model simulation that simulates energy, hydrology, and C processes at high latitudes such as permafrost, soil temperature, and soil C interactions. The model was optimized with a detailed calibration against several data sets for the northern high latitudes (Guimberteau et al., 2018). It includes land-atmosphere CO₂ exchange on a 30-min to daily time step. The model simulation was performed using ERA5 reanalysis data (air temperature, humidity, pressure, shortwave and longwave incoming radiation, wind vectors, and precipitation; from the European Center for Medium-Range Weather Forecasts) at 1° × 1° resolution, with a fixed land cover map for the year 2010 [HYDE3.2; Klein Goldewijk et al. (2017)]. The model spin-up consisted of (a) 150 years looping the years from 1979 to 1990 to reach the equilibrium of the physical variables, (b) 10,000 years of soil accumulation until the soil C pool is saturated, and (c) another 150 years to reach the equilibrium of the physical variables with the soil C pools. The transient run was conducted from 1979 to the end of 2021. The global annual atmospheric CO₂ concentration for the model transient run was prescribed from the Mauna Loa Observatory record (NOAA, Earth System Research Laboratories). Daily flux rates were aggregated to monthly fluxes.

We simulated 2021 CO₂ fluxes (a) with the actual climate forcing in each year from 1979 to 2021, so that the fluxes in 2021 include the carry-over effect of the 2020 HW (HW simulation), and (b) with a set of counterfactual scenarios, where the 2020 climate forcing was replaced by each of the previous five years (2015–2019) without an HW event (noHW simulations; Table 1). Thus, the simulated fluxes for 2021 in (b) do not include the HW legacy effects from the previous year and provide a range of possible CO₂ fluxes, as if the HW had not occurred in 2020, but with plausible climate changes in recent years. The differences between these two sets of simulations [(a)–(b)] are therefore defined as the 2020 HW legacy effects remaining in 2021 (Table 1). By averaging 5 years of
<table><caption>**Table 1**
The Experimental Setup for the Legacy Effect of 2020 HW in 2021</caption><thead><tr><th>Climate of the year 2020</th><th>Climate of the year 2021</th><th>Simulation</th><th>Legacy in 2021</th></tr></thead><tbody><tr><td>2020</td><td>2021</td><td>NEE<sub>HW</sub></td><td>NEE<sub>legacy</sub> = NEE<sub>HW</sub> - NEE<sub>noHW</sub></td></tr><tr><td>2015</td><td>2021</td><td>NEE<sub>noHW</sub> (average of five simulations)</td><td>NEE<sub>noHW,2015</sub></td></tr><tr><td>2016</td><td>2021</td><td></td><td>NEE<sub>noHW,2016</sub></td></tr><tr><td>2017</td><td>2021</td><td></td><td>NEE<sub>noHW,2017</sub></td></tr><tr><td>2018</td><td>2021</td><td></td><td>NEE<sub>noHW,2018</sub></td></tr><tr><td>2019</td><td>2021</td><td></td><td>NEE<sub>noHW,2019</sub></td></tr></tbody></table>

Note. Simulations of NEE using the climate data of the actual years are denoted as NEE<sub>HW</sub>. The climate forcing for 2020 was replaced with the climate of the recent years (2015–2019) for noHW simulations, and the average of these five simulations is denoted as NEE<sub>noHW</sub>. The difference between the two (NEE<sub>HW</sub> – NEE<sub>noHW</sub>) is the legacy effect (NEE<sub>legacy</sub>) of 2020 HW in 2021.

simulation results (e.g., NEE<sub>noHW</sub>) instead of estimating NEE<sub>noHW</sub> using an averaged climate of 5 years, we included the spatiotemporal variability between years that may influence the magnitude of the estimated legacy effects. Since a positive NEE refers to the net CO₂ emissions to the atmosphere, a positive NEE<sub>legacy</sub> indicates more CO₂ emissions or less CO₂ uptake in 2021 as a result of the 2020 HW event. In addition to NEE, we analyzed the legacy effects of the component fluxes, R<sub>h,legacy</sub> and NPP<sub>legacy</sub>, to determine whether the CO₂ legacy responses originate from soil or vegetation responses. Fluxes were simulated for eight different PFTs in extra-tropical regions (temperate needleleaf evergreen, temperate broadleaf evergreen, temperate broadleaf deciduous, boreal needleleaf evergreen, boreal broadleaf deciduous, boreal needleleaf deciduous, C3 grass, and C4 grass), and we categorized them into two functional groups, forests and grasslands, to compare their immediate and legacy responses to the HW. In addition, we analyzed the legacy effects of soil temperature, that is, SoilT<sub>HW</sub> – SoilT<sub>noHW</sub>, at 100 cm depth for forests and 20 cm depth for grasslands (SoilT<sub>legacy</sub>), and soil water and ice content (SoilWater<sub>legacy</sub> and SoilIce<sub>legacy</sub>), and their combined total soil water content (SoilWater.tot<sub>legacy</sub>) in the upper 0–100 cm for forests and 0–20 cm for grasslands, three discrete soil C stocks (C<sub>active,legacy</sub>, C<sub>slow,legacy</sub>, and C<sub>passive,legacy</sub>) and plant biomass (Biomass<sub>legacy</sub>) to investigate whether the 2020 HW had carry-over effects on the soil environment and plant and soil C stocks until the end of 2021, and whether these legacy effects in the soils and plants are associated with those of fluxes, that is, NEE<sub>legacy</sub>, R<sub>h,legacy</sub> and NPP<sub>legacy</sub>. Different soil depths were chosen for forests (0–100 cm) and grasslands (0–20 cm) because the maximum root depths in the model are limited to 100–125 cm for forests and 25–100 cm for grasslands in the model, and the root depths, density and biomass are simulated within these depths.

To show the deviation of climate, soil conditions and land-atmosphere exchanges of each year from the long-term trends, anomalies of fluxes (e.g., NEE<sub>anomaly</sub>) and environmental variables (e.g., SoilT<sub>anomaly</sub>) relative to 1979–2019 were calculated after removing the monthly linear trends of 1979–2019. A positive NEE<sub>anomaly,2020</sub> indicates a larger CO₂ source (or a smaller CO₂ uptake) in 2020 relative to the 1979–2019 detrended mean, and a positive SoilT<sub>anomaly,2020</sub> indicates a warmer soil temperature in 2020 relative to the 1979–2019 detrended mean. Results are presented by quarter: q1 is January–March; q2 is April–June; q3 is July–September; and q4 is October–December. Anomalies and legacies of a given period can be written as AirT<sub>anomaly,2020,q1</sub> for AirT<sub>anomaly</sub> during the first quarter of 2020, and NEE<sub>legacy,2021,q1</sub> for NEE<sub>legacy</sub> during the first quarter of 2021.

Due to the limited number of simulations, that is, five simulation sets per grid cell, we pooled all grid cells of each biome type (space-for-time) within the Siberian domain (>50°N, 40–180°E). The whole study area was affected by a wide range of climate anomalies in 2020, from strong HW to no HW in different seasons, so the gradient of the responses can show us the drivers of the legacy effects. We categorized the grid cell values into four areal groups depending on the z-scores of AirT<sub>anomaly</sub> in 2020 (Figure 1); “noHW”: z-scores of AirT<sub>anomaly</sub> less than 2 during q1 and q2 in 2020 (2203 grid cells); “Q1”: z-scores greater than 2 during q1 (1491 grid cells); “Q2”: z-scores greater than 2 during q2 (1157 grid cells); “Q1,Q2”: z-scores greater than 2 during q1 and q2 (561 grid cells). Together with HW, snow can be another critical factor influencing the soil environment and flux rates. To investigate the effects of HW legacy and snow separately, we first divided the study area into four HW groups. Then, we divided each group into two subgroups based on snow mass (kg m⁻²): one subgroup with more snow during q4 of 2020 (Snow<sub>anomaly,2020,q4</sub> > 0) and the other with less snow (Snow<sub>anomaly,2020,q4</sub> < 0).
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Heatwave area groups within the study domain >50°N, 40–180°E divided by z-scores of AirT<sub>anomaly</sub> (ERA5 reanalysis) during 1979–2019. “noHW”: z-scores of AirT<sub>anomaly</sub> less than 2 during q1 and q2 in 2020; “Q1”: z-scores greater than 2 during q1 in 2020; “Q2”: z-scores greater than 2 during q2 in 2020; “Q1,Q2”: z-scores greater than 2 during q1 and q2 in 2020.</figcaption></figure>

# 3. Results and Discussion

## 3.1. Legacy Effects in Soil Environment

In central Siberia, HW was particularly strong and persistent in 2020 (HW area groups “Q2” and “Q1,Q2” in Figures 1, 2a, and 2f). The “Q2” area experienced a strong HW event only in the second quarter of 2020 by definition (z-scores greater than 2), but was also exposed to warmer air temperatures than usual during the first quarter and thus showed higher a higher AirT<sub>anomaly</sub> than the “noHW” area during q1 (Figure 2a). There were no significant changes in soil temperature or water/ice content during q1, but persistent HW warmed soil temperature during q2, especially for “Q2” and “Q1,Q2” areas (SoilT<sub>anomaly,q2</sub>, Figures 2b and 2g). Correspondingly, soil ice melted (decreased SoilIce<sub>anomaly,q2</sub>) and soil became wetter (increased SoilWater<sub>anomaly,q2</sub>), but without changes in the total soil water content (SoilWater.tot<sub>anomaly,q2</sub>; Figures 2c–2e and 2h–2j).

The ORCHIDEE simulations showed that HW legacy effects continued beyond 2020, both above- and below-ground. The soil temperature remained warm in 2021 (positive SoilT<sub>legacy</sub>; Figure 3a and Figure S1a in Supporting Information S1), even though the air temperature during q1 of 2021 was lower than in 2020 and the average of 2015–2019 (Figure S2 in Supporting Information S1). This suggests that the warmer soil in 2021 was due to the effects of climate anomalies in 2020. In particular, the HW area “Q2”, the area that experienced HW during q2 of 2020, showed 1.23 and 1.13°C warmer soil temperatures (greater SoilT<sub>legacy</sub>) compared to “noHW” during q1 of 2021 in forest and grassland soils, respectively (Figures 3a and 3e, Figure S1a, and Table S1 in Supporting Information S1). Warmer soil (positive SoilT<sub>legacy</sub>) combined with more soil water (positive SoilWater<sub>legacy</sub>) and less soil ice (negative SoilIce<sub>legacy</sub>) suggested that warmer soil temperature melted soil ice and increased soil water availability by 19.43 and 1.99 kg m<sup>-2</sup> in forest and grassland soils, respectively (Figures 3a–3c, Figures S1a–S1c, and Table S1 in Supporting Information S1). However, not all of the thawed soil water remained in the soil but was lost through runoff, and this was shown as a reduced SoilWater.tot<sub>legacy</sub> (Figures 3d and 3h). In permafrost-affected soils of northern Siberia, thaw depth (TD; the depth to the frozen ground) increased in accordance with warming (Figure S1d in Supporting Information S1). Legacy effects in soil-related variables were strongest during q1 of 2021, and decreased over time, as the effects of the 2021 climate overrides the legacy effects (Figure 3, Table S1, and Figure S1 in Supporting Information S1). Exceptionally, however,
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$TD_{\text{legacy}}$ persisted until the end of 2021 (Figures S1d and S1h in Supporting Information S1). These prolonged effects of $TD_{\text{legacy}}$ can be attributed to the delayed response of the deep soil (Biskaborn et al., 2019).

The warmer and wetter (earlier thawed) soils in early 2021 were influenced not only by the HW in 2020 but also by deeper snow depth ($\text{Snow}_{\text{anomaly}}$) in late 2020: Increases in $\text{SoilT}_{\text{legacy}}$ and $\text{SoilWater}_{\text{legacy}}$, and decreases in $\text{SoilIce}_{\text{legacy}}$ were enhanced by more snow during q4 in 2020 (greater $\text{Snow}_{\text{anomaly,q4}}$; Figure 3 and Figure S3 in Supporting Information S1). This snow effect was large in q1 and q2 of 2021, and diminished over time (warmer and wetter soil due to more snow shown as positive values in Figure S3 in Supporting Information S1). More snow in winter can keep soil temperature warm and soil water unfrozen by insulation, and increase soil water availability during snowmelt (Sutinen et al., 2008; Zhao et al., 2022). It emphasizes that HW or warm air temperature is not the only prerequisite for warmer and wetter soil legacy effects in the following year, but the interplay of temperature and snow is critical (Beer et al., 2018; Grünberg et al., 2020; Koven et al., 2013; Loranty et al., 2018; Sutinen et al., 2008; Zhao et al., 2022). Although the liquid soil water content ($\text{SoilWater}_{\text{legacy}}$) was higher in 2021 due to the HW in 2020, the total soil water storage (the combined $\text{SoilWater}_{\text{legacy}}$ and $\text{SoilIce}_{\text{legacy}}$; $\text{SoilWater.tot}_{\text{legacy}}$) was reduced even more by more snow (Figure S3c in Supporting Information S1), suggesting that some of the melted soil water was lost during the HW. In permafrost-affected regions, ice can be an important source of water during the growing season (Gao et al., 2021; Young-Robertson et al., 2017), and significant ice melt and loss induced by climate extremes can reduce terrestrial water storage and increase water flow to aquatic systems (Hiyama et al., 2023; Jin et al., 2022; St. Jacques & Sauchyn, 2009). In addition, reduced soil water in the active layer can reduce permafrost ice content (W. Wang et al., 2018). Although not shown in our results, significant ice melt can trigger drastic and sometimes irreversible processes, such as thaw slumps (Lewkowicz & Way, 2019; Runge et al., 2022) and lake drainage (Lara et al., 2021; Nitze et al., 2020). More research is needed to clarify the importance of extreme events to better understand such rapid and irreversible landscape processes.

## 3.2. Legacy Effects in CO₂ fluxes

As the 2020 climate affected the soil environment especially in early 2021, the $CO_2$ fluxes of 2021 were affected accordingly. Central Siberia, where warmer and wetter soil conditions persisted in 2021 due to the 2020 climate ("Q2" and "Q1,Q2" areas), showed more $CO_2$ emission (positive $NEE_{\text{legacy,q1}}$) in q1 of 2021 (Figure 4a). Most of the signals of $NEE_{\text{legacy,q1}}$ came from $R_{\text{h,legacy,q1}}$ (Figures 4a–4c and Figure S4 in Supporting Information S1), as there was little photosynthetic activity during this season. Within the Siberian domain, the HW area "Q2" showed $0.04 \text{ gC m}^{-2} \text{ day}^{-1}$ more $CO_2$ emissions ($NEE_{\text{legacy,q1}}$; $0.04 \text{ gC m}^{-2} \text{ day}^{-1}$ greater $R_{\text{h,legacy,q1}}$) compared to the "noHW" area. In forests, HW area "Q2" showed $0.03 \text{ gC m}^{-2} \text{ day}^{-1}$ more $CO_2$ emissions ($R_{\text{h,legacy,q1}}$), while grasslands showed $0.05 \text{ gC m}^{-2} \text{ day}^{-1}$ more emissions compared to "noHW" area during q1 (Table S2 in Supporting Information S1). Because soil variables ($\text{SoilT}_{\text{legacy,q1}}$ and $\text{SoilWater}_{\text{legacy,q1}}$) largely influenced $CO_2$ emissions ($R_{\text{h,legacy,q1}}$), more snow (increasing $\text{SoilT}_{\text{legacy,q1}}$ and $\text{SoilWater}_{\text{legacy,q1}}$) also increased $R_{\text{h,legacy,q1}}$ (Figure 4c). However, $\text{NPP}_{\text{legacy,q1}}$ was reduced by more snow (Figure 4b), which can be explained by the delayed onset of photosynthesis due to the longer time needed for snowmelt.

Similar positive $NEE_{\text{legacy}}$ and $R_{\text{h,legacy}}$ patterns were found in q2 but less strong than in q1 (Figure 4 and Table S2 in Supporting Information S1), as legacy effects in the soil environment ($\text{SoilT}_{\text{legacy}}$ and $\text{SoilWater}_{\text{legacy}}$) decreased with time (Figure S1 in Supporting Information S1). Overall, the HW area "Q2" emitted $0.01 \text{ gC m}^{-2} \text{ day}^{-1}$ more $CO_2$ ($NEE_{\text{legacy,q2}}$; $0.01 \text{ gC m}^{-2} \text{ day}^{-1}$ greater $R_{\text{h,legacy,q2}}$) compared to the "noHW" area. Although the overall $CO_2$ legacy patterns were similar to q1, the legacy effects in the $\text{NPP}$ during q2 differed by biome type: in forests, more $CO_2$ emission (positive $R_{\text{h,legacy,q2}}$; $0.007 \text{ gC m}^{-2} \text{ day}^{-1}$) was largely offset by increased $CO_2$ uptake (positive $\text{NPP}_{\text{legacy,q2}}$; $0.02 \text{ gC m}^{-2} \text{ day}^{-1}$; Figure 4, Table S2, and Figure S4 in Supporting Information S1). This result suggests that the warmer soil temperature and increased soil water content induced by the 2020 climate created favorable conditions not only for soil C decomposition and respiration but also for photosynthesis especially in forests (Moyes et al., 2015; Reich et al., 2018), which did not experience suppressed

**Figure 2.** Anomalies in air temperature and soil variables (a, f) $\text{AirT}_{\text{anomaly}}$ (°C), (b, g) $\text{SoilT}_{\text{anomaly}}$ (°C), (c, h) $\text{SoilWater}_{\text{anomaly}}$ ($\text{kg m}^{-2}$), and (e, j) $\text{SoilWater.tot}_{\text{anomaly}}$ ($\text{kg m}^{-2}$) during (a–e) q1 and (f–j) q2 of 2020 by HW area groups based on $\text{AirT}_{\text{anomaly}}$ of 2020 as described in Figure 1 on the X-axis. "noHW": z-scores of $\text{AirT}_{\text{anomaly}}$ less than 2 during q1 and q2 in 2020; "Q1": z-scores greater than 2 during q1 in 2020; "Q2": z-scores greater than 2 during q2 in 2020; "Q1,Q2": z-scores greater than 2 during q1 and q2 in 2020. The top, middle and bottom lines of the boxes represent the 75% quantile, the median and the 25% quantile, and the whiskers represent 1.5 times the interquartile range.
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**Figure 3.** Legacy in soil variables (a, e) SoilT<sub>legacy</sub> (°C), (b, f) SoilWater<sub>legacy</sub> (kg m<sup>-2</sup>), (c, g) SoilC<sub>legacy</sub> (kg m<sup>-2</sup>), and (d, h) SoilWater.tot<sub>legacy</sub> (kg m<sup>-2</sup>) during (a–d) q1 and (e–h) q2 of 2021 by HW area groups based on AirT<sub>anomaly</sub> of 2020 as described in Figure 1 on the X-axis. “noHW”: z-scores of AirT<sub>anomaly</sub> less than 2 during q1 and q2 in 2020; “Q1”: z-scores greater than 2 during q1 in 2020; “Q2”: z-scores greater than 2 during q2 in 2020; “Q1,Q2”: z-scores greater than 2 during q1 and q2 in 2020. Box plots were drawn separately by Snow<sub>anomaly</sub> in q4 of 2020, anomalies below 0 with light colors, and anomalies above 0 with dark colors. The top, middle and bottom lines of the boxes represent the 75% quantile, the median and the 25% quantile, and the whiskers represent 1.5 times the interquartile range. Black dots are the medians of both Snow<sub>anomaly</sub> groups.

photosynthesis in 2020 (Figure S5 in Supporting Information S1). Although sustained high air temperature (HW) developed a climatic water deficit (CWD; potential evapotranspiration–actual evapotranspiration) during the growing season in 2020 (Figure S6 in Supporting Information S1), NPP in forests remained high in the late growing season (Figure S5 in Supporting Information S1) possibly due to deep roots that can access soil water (Schenk & Jackson, 2002; Zeng, 2001) and an effective water use efficiency strategy (Teuling et al., 2010). This high NPP induced by HW in forests in 2020 increased the plant biomass by 9.54 gC m⁻² in the “Q2” area compared to the “noHW” area in 2021 (Figures 5a and 5c, Table S2, and Figures S7 and S8 in Supporting Information S1). The increase in the legacy in plant biomass was stronger in the “Q1,Q2” area than in the “Q2” area (Figures 5a and 5c, Table S2, Figures S7 and S8 in Supporting Information S1), suggesting that forests benefited
<figure><img src="image_23.png" /><figcaption>**Figure 4.** Legacy in fluxes (a,d) NEE<sub>legacy</sub> (gC m<sup>-2</sup> day<sup>-1</sup>), (b, e) NPP<sub>legacy</sub> (gC m<sup>-2</sup> day<sup>-1</sup>), and (c, f) R<sub>h,legacy</sub> (gC m<sup>-2</sup> day<sup>-1</sup>), during (a–d) q1 and (e–h) q2 of 2021 by HW areal groups based on the AirT<sub>anomaly</sub> of 2020 as described in Figure 1 on the X-axis. “noHW”: z-scores of AirT<sub>anomaly</sub> less than 2 during q1 and q2 in 2020; “Q1”: z-scores greater than 2 during q1 in 2020; “Q2”: z-scores greater than 2 during q2 in 2020; “Q1,Q2”: z-scores greater than 2 during q1 and q2 in 2020. Box plots were drawn separately by Snow<sub>anomaly</sub> of q4 in 2020, anomalies below 0 with pale colors, and anomalies above 0 with dark colors. The top, middle, and bottom lines of the boxes represent the 75% quantile, the median and the 25% quantile, and the whiskers represent 1.5 times the interquartile range. Black dots are the medians of both Snow<sub>anomaly</sub> groups.</figcaption></figure>

more from longer HW events in terms of accumulating C. Among six different forest PFTs, boreal needleleaf evergreen (PFT no. 7) showed the largest positive NPP<sub>legacy</sub> along with the increased Biomass<sub>leaf,legacy</sub> and LAI<sub>legacy</sub> (Figures S8, S9, and S10 in Supporting Information S1). Boreal deciduous species (PFT no. 8 and 9) showed increased Biomass<sub>legacy</sub>, but LAI<sub>legacy</sub> and NPP<sub>legacy</sub> did not show noticeable changes (Figures S9 and S10 in Supporting Information S1). Deciduous species may have benefited from 2020 HW and increased biomass, but leaf biomass and LAI, and thus, NPP of 2021 may be less influenced by 2020 climate. Evergreen and deciduous species may show different responses to warming (Dusenge et al., 2020) due to their different traits (Krishna & Chandra Garkoti, 2022) and unequal C allocation (Kannenberg et al., 2019).

In contrast to forests, grasslands in central Siberia (“Q2” and “Q1,Q2” areas) showed seasonal carry-over effects after 2020 HW, with an increase in CO₂ uptake during HW that reversed into a decrease in CO₂ uptake after HW (Figure S5 in Supporting Information S1). This was likely due to increased water demand (Bastos et al., 2020; Lian et al., 2020; Wolf et al., 2016) and vegetation legacy effects (Lian et al., 2021; Zani et al., 2020). These seasonal legacy effects from 2020 affected NPP until the following year, although insignificantly: grasslands exhibited reduced CO₂ uptake (NPP<sub>legacy</sub> of the “Q2” area lower by 0.01 gC m⁻² day⁻¹ compared to that of the “noHW” area) in 2021 (Table S2 and Figure S4 in Supporting Information S1) together with reduced biomass (Biomass<sub>legacy</sub> of the “Q2” area lower by 0.162 gC m⁻² compared to that of the “noHW” area; Figures 5a and 5c, Table S2, and Figure S8 in Supporting Information S1), even under warmer and wetter soil conditions (Figure S1 in Supporting Information S1). This suggests that grasslands that experienced seasonal legacy effects in 2020 did not fully recover to the previous levels until 2021 (Lian et al., 2021) or that their sensitivity to climate has changed
<figure><img src="image_24.png" /><figcaption>**Figure 5.** Legacy in (a, c) plant Biomass<sub>legacy</sub> (gC m<sup>-2</sup>), (b, d) soil C pool C<sub>soil,legacy</sub> (gC m<sup>-2</sup>) during (a–b) q1 and (c–d) q2 of 2021 by HW areal groups based on the AirT<sub>anomaly</sub> of 2020 as described in Figure 1 on the X-axis. “noHW”: z-scores of AirT<sub>anomaly</sub> less than 2 during q1 and q2 in 2020; “Q1”: z-scores greater than 2 during q1 in 2020; “Q2”: z-scores greater than 2 during q2 in 2020; “Q1,Q2”: z-scores greater than 2 during q1 and q2 in 2020. Box plots were drawn separately by Snow<sub>anomaly</sub> of q4 in 2020, anomalies below 0 with pale colors, and anomalies above 0 with dark colors. The top, middle and bottom lines of the boxes represent the 75% quantile, the median and the 25% quantile, and the whiskers represent 1.5 times the interquartile range. Black dots are the medians of both Snow<sub>anomaly</sub> groups.</figcaption></figure>

after experiencing HW in 2020 (Bastos et al., 2021; Wu et al., 2022). Grasslands typically contribute significantly to negative GPP extremes (Gampe et al., 2021), because these ecosystems are less resilient to HW and drought (Bastos et al., 2021; Flach et al., 2018, 2021; Salomón et al., 2022; Shekhar et al., 2020; Xiao et al., 2023; Xu et al., 2020).

These changes in CO₂ fluxes resulted in a reduced soil C pool in 2021 due to the 2020 HW (C<sub>soil,legacy,q2</sub> of the “Q2” area lower by 3.57 and 10.69 gC m<sup>-2</sup> in forests and grasslands, respectively, compared to that of the “noHW” area; Table S2 in Supporting Information S1, Figures 5b and 5d). When the legacy effects in plant biomass and soil C pool were combined, forests of the “Q2” area gained 5.97 gC m<sup>-2</sup> more C than the “noHW” area, while grasslands lost 10.85 gC m<sup>-2</sup> (Table S2 in Supporting Information S1, Figure 5). Due to the increased leaf biomass in forests (leaf part of Biomass<sub>legacy</sub> in Figure S8 in Supporting Information S1), an increased amount of litterfall and soil C input is expected to compensate for the reduced soil C pool in the following years. However, increased plant biomass in wood and roots (sapwood, heartwood, and roots of Biomass<sub>legacy</sub> in Figure S8 in Supporting Information S1) may persist until upcoming disturbances occur.

These results indicate that the effects of extreme climates in one season can persist above- and below-ground processes, soil and plant C pools and CO₂ fluxes in the following season and even in the following year, and that these seasonal and interannual legacy effects can differ across biomes. However, it is noteworthy that the responses can differ by PFTs (Qu, Dong, et al., 2024), and drought can increase soil nutrient availability (Bloor & Bardgett, 2012; de Vries et al., 2016), potentially leading to a positive legacy effect in NPP, especially if the recovery is rapid (Hahn et al., 2021).

## 4. Limitations

We estimated the legacy effects of the 2020 Siberian HW as a case study. This HW event was one of the most widespread, intense, and long-lasting events, so that its effects on the ecosystem were easier to detect than local, weak, and short events. However, this HW event does not represent all possible ecosystem responses of other HW events. For example, the timing of HW is one of the critical factors that determines the direction and intensity of its effects (Qu, Chen, et al., 2024; Sippel et al., 2016), and the Siberian 2020 HW occurred in the winter/spring
period when snow was on the ground and soil was frozen underground. Thus, HW melted snow and soil ice and provided sufficient soil water for vegetation during this long event, and HW did not negatively affect CO₂ fluxes until the late in the growing season. When HW occurs when soil water is scarce (when HW is combined with drought), CO₂ fluxes—especially photosynthesis—may be negatively affected (Ciais et al., 2005; Zscheischler et al., 2014). In addition, we presented the results focusing on the Arctic/boreal forests and grasslands, and other biome types or PFTs may respond differently to HW (Xu et al., 2020).

Legacy effects can be as strong as the direct climate effects. In the Eurasian steppe, the legacy effects in CO₂ fluxes were similar (Qu et al., 2018) or lower (Dong et al., 2022) compared to the direct climatic effects of the previous year. Yu et al. (2022) estimated legacy effects using flux observations and random forest, and reported that the legacy effects in GPP may be comparable to the direct climate effects in temperate forests, but the responses may differ by species composition, forest age and year. Our results showed that the legacy effects of the 2020 HW on CO₂ fluxes in 2021 can be *ca.* 30% more CO₂ emissions in q1 and *ca.* 10% more emissions in q2 of 2021. This magnitude may be similar or lower compared to other studies considering different effect sizes and missing observations. If it is considered underestimated, one of the reasons may be the timing of the HW, which did not significantly damage the ecosystem in 2020, but also partly due to potential underestimation of legacy effects in the NPP, as land surface models may not well represent memory effects over multiple years (Bastos et al., 2021; Lian et al., 2021). This underestimation may be even greater, if ecosystems undergo structural changes, such as tree mortality, disturbance, compositional changes (Holm et al., 2022; Li et al., 2023; Liu et al., 2023; Ruehr et al., 2019), or soil collapse (Runge et al., 2022; Turetsky et al., 2020).

Our results showed spatial variation due to different legacy effects in soil environment, CO₂ fluxes, and biome type, and adding more complexity, such as soil nutrients (e.g., nitrogen), different responses by taxa, succession after disturbance (e.g., fire), ecosystem age, and site conditions, may further increase spatial and temporal variation (Kannenberg et al., 2020; Yu et al., 2022). Increasing frequency and intensity of climate extremes (Baldwin et al., 2019; Dobricic et al., 2020; Fischer et al., 2014; Kornhuber & Tamarin-Brodsky, 2021; Rasmijn et al., 2018; S. I. Seneviratne et al., 2022), especially with two or more consecutive events, can have stronger effects on ecosystem (Matusick et al., 2018), with longer recovery times (Li et al., 2023; Schwalm et al., 2017), and reduced C sink capacity of terrestrial ecosystems (Reichstein et al., 2013).

## 5. Conclusions

During and after the 2020 HW, both NPP and R<sub>h</sub> showed “first enhanced and then suppressed fluxes” in 2020, and this seasonal legacy effect was mostly driven by vegetation activities rather than soil processes (Kwon et al., 2021). In this study, our model results showed that the legacy effects of the 2020 HW in 2021 were driven by both soil processes and vegetation functions; (a) the 2020 climate, especially the high air temperature (HW) in early 2020 combined with more snow in late 2020, kept the soil warmer and wetter until early 2021, and (b) this warm and wet soil condition in 2021 due to the 2020 climate promoted higher CO₂ emissions (legacy in flux; R<sub>h,legacy</sub>). This led to a decreased soil C pool (C<sub>soil,legacy</sub>) in 2021. CO₂ uptake (NPP<sub>legacy</sub>) was also affected but differed by biome type: warmer and wetter soil environments enhanced photosynthesis in forests in 2021, where 2020 NPP was not suppressed by the seasonal legacy effects, whereas grasslands showed suppressed 2021 NPP in continuation of the negative seasonal legacy effects from 2020. These changes can be attributed to increased plant biomass (Biomass<sub>legacy</sub>) in forests and decreased plant biomass in grasslands in 2021. Overall, forest gained 6 gC m<sup>-2</sup> on land in early 2021 due to the 2020 HW (Biomass<sub>legacy</sub> and C<sub>soil,legacy</sub> combined), while grasslands lost 10.9 gC m<sup>-2</sup>. The net effect of the 2020 HW on CO₂ fluxes in 2020 was 3 gC m<sup>-2</sup> year<sup>-1</sup> less CO₂ uptake (Kwon et al., 2021), and adding the legacy effects until the first half of 2021 led to 4.1 gC m<sup>-2</sup> more CO₂ emission (less CO₂ uptake) for the most HW affected area (“Q2” area). These results emphasize that an extreme climate event in one season can affect the CO₂ fluxes of the following season and year through both above- and below-ground processes. The legacy effects weakened during 2021 but remained high in TD until the end of 2021, emphasizing that deep soils can be affected for a longer time.

## Data Availability Statement

The simulation data that support the findings of this study are stored at Kwon (2025).
Acknowledgments

M.J.K. was supported by the DFG excellence cluster CLICCS (EXC2037/1). C.B. acknowledges the financial support by Deutsche Forschungsgemeinschaft (DFG-BE 6485/1-1, DFG-BE 6485/4-1, heisenberg program 508047523). We acknowledge the technical support by CEN-IT, which is the central IT service provider of the Center for Earth System Research and Sustainability at Universität Hamburg. A.B. was partly funded by the European Union (ERC StG, ForExD, grant agreement No. 101039567). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. Open Access funding enabled and organized by Projekt DEAL.

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