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Glass eels collection (Anguilla anguilla, L.)Three hundred European glass eels ( More

External datasetsWoody biomass carbon dataThe dataset by ref. 16 maps annual global woody biomass carbon densities for 2000–2019 at a spatial resolution of ~10 km. The annual estimates represent averages for the tropical regions and growing-season (April–October) averages for the extra-tropical regions. Ref. 16 analyse global trends of gains and losses in woody biomass carbon for 2000–2019. Overall, they find that grid cells with (significant) net gains of vegetation carbon are by a factor of 1.4 more abundant than grid cells with net losses of vegetation carbon, indicating that there is a global greening trend when only considering the areal extent of biomass gains and not the magnitude of carbon gains. Their regionally distinct analysis of trends shows that almost all regions, except for the tropical moist forests in South America and parts of Southeast Asia, experienced net gains in biomass carbon. On the country scale, the largest net increase in biomass carbon is shown in China, which is mainly attributed to the large-scale afforestation programs in the southern part of the country and increased carbon uptake of established forests. On the other hand, the largest vegetation carbon losses are shown for Brazil and Indonesia, which is partly attributed to deforestation, degradation, and drought events. All of the mentioned trends have been found to be significant16. The decreasing carbon sink in Brazil is in line with ref. 44, who, considering both natural and anthropogenic fluxes, show that the southeastern Amazon has even turned from a carbon sink to a carbon source, mainly owing to fire emissions from forest clearing. Isolating carbon fluxes in intact, old-growth Amazonian rainforests (i.e., SLAND,B), ref. 45 also find evidence for a significantly decreasing carbon sink due to the negative effects of increasing temperatures and droughts on carbon uptake since the 1990s.The dataset was remapped to the BLUE resolution of 0.25∘ through conservative remapping (i.e., area-weighted averaging).ERA-5 dataThe ERA-5 variables were downloaded from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/cdsapp#!/home). Monthly air temperature (Ta) at 2 m height was averaged over each year, and annual precipitation was calculated by taking the sum of the monthly total precipitation (P). Both variables were regridded from the original resolution of ~0.1° to 0.25° resp. to the TRENDY resolution of 0.5° through conservative remapping.TRENDY dataWe used the TRENDY model ensemble version 8 (conducted for the 2019 GCB; ref. 8). We used net biome production (NBP) and annual vegetation carbon stocks (cVeg) for 2000–2018 from four different model setups (S2, S3, S5, and S6) and eight resp. 13 DGVMs (depending on the data available). The selection of DGVMs is done as in ref. 19 (Supplementary Tab. 3), but we included one additional model (ISAM) for the S2 simulations. The terrestrial biomass carbon sink (SLAND,B) was calculated for 13 DGVMs following the GCB 2020 approach, i.e., from the S2 simulation, which is the simulation without LULCC (i.e., fixed pre-industrial land cover) under transient environmental conditions (climate, nitrogen deposition, CO2 evolution). SLAND,B is the annual difference of cVeg and makes no statements about the further fate of biomass if cVeg decreases. SLAND,B, therefore, should not be interpreted as equivalent to the flux to/from the atmosphere, since parts of cVeg may be transferred to litter, dead wood, or soil. The same applies to our BLUE estimates of SLAND,B, ensuring comparability between our BLUE estimates and the TRENDY estimates. Increases (decreases) of cVeg between two years are a net uptake (release) of carbon from the terrestrial biosphere. The global sums of biomass carbon stocks under transient climate and CO2 were calculated from the S3 setup (LULCC under historical environmental conditions), whereas the S5 setup provides biomass carbon under constant present-day environmental forcing (closest to the classical bookkeeping approach). In line with the GCB, ELUC was calculated under historical environmental conditions as the difference in NBP between the S2 and S3 simulations (ELUC = NBP_S2 – NBP_S3). ELUC under constant present-day environmental forcing was calculated as the difference in NBP between the S6 (fixed pre-industrial land cover under present-day environmental forcing) and S5 simulations (ELUC = NBP_S6 – NBP_S5)19. All datasets were remapped to a common resolution of 0.5∘ through conservative remapping (area-weighted average) for the data analysis.Assimilation of observed woody biomass carbon in BLUEThe observed woody biomass carbon densities by ref. 16 are assimilated in BLUE in several steps.Carbon transfer in the default setup of BLUEThe BLUE simulation is started in AD 850. Biomass and soil vegetation carbon densities are based on ref. 17, which are converted to exponential time constants. A detailed explanation of the exponential model can be found in ref. 5.While in the default setup, changes are only due to LULCC, our assimilation approach now introduces environmental effects on woody vegetation carbon by assimilating the observed woody biomass carbon densities in BLUE from 2000 onward according to the methodological considerations explained below.Calculation of woody biomass carbon densities for different land cover types and PFTsWithin each 0.25° cell of the global grid, the (remapped) woody biomass carbon density from ref. 16 must be the sum of woody biomass carbon stored in all woody PFTs of all woody land cover types. The distribution of the woody biomass carbon across PFTs and land cover types is achieved by distributing the observed (i.e., actual) woody biomass carbon densities (ρBa) from ref. 16 across the two land cover types (j) and the eight PFTs (l) that can be woody vegetation (primary land, called virgin, “v” in BLUE and secondary, “s”, land) according to the fraction of total woody biomass carbon (fB) contained in each land cover type and each PFT (fB,j,l) as estimated by BLUE. fB,j,l varies for different PFTs and land cover types, depending on their history of LULCC and their potential for carbon uptake (i.e., the potential carbon densities).fB,j,l is extracted from the default simulations for the first year of the time series (i.e., 2000) and calculated for subsequent years from the BLUE simulations using the assimilated woody vegetation carbon densities for that year:$${f}_{B,j,l}(t)=frac{{C}_{B,j,l}(t)}{{C}_{B}(t)}$$
(1)
where CB is the woody biomass carbon stock.Consequently, the assimilated woody biomass carbon stock per cover type and PFT (CB_as,j,l) at each time step can be calculated as:$${C}_{B_as,j,l}(t)={rho }_{Ba}(t);*;A;*;{f}_{B,j,l}(t)$$
(2)
with j{v, s}; l{1. . 8}; t{2000. . 2019}. A is the area per grid cell.Thresholds for excluding inconsistent woody biomass carbon densitiesWe eliminate unrealistically large values for woody biomass carbon densities that our assimilation framework produces. Woody biomass carbon densities in BLUE that exceed the highest value (~374 t ha−1) of the original dataset indicate inconsistencies between the observed woody biomass carbon estimates and the fractional grid cell areas per PFT and land cover types that BLUE simulates. To account for uncertainties related to the criteria for exclusion of grid cells, multiple threshold approaches are applied and the results are compared. To maintain a temporally and spatially consistent time series of woody biomass carbon, grid cells that are excluded according to the chosen threshold approach are interpolated through linear barycentric interpolation. A first approach relies on a uniform upper threshold of More

We focused on the Northern High Latitudes (NHL, latitude > 50°N, excluding Greenland) due to their importance for carbon (CO2-C, the same hereafter)-climate feedbacks in the Earth system. To minimize the potential human influence on the CO2 cycle, we excluded areas under agricultural management (croplands, cropland/natural vegetation mosaic, and urban types), and considered only pixels of natural vegetation defined from the MODIS MCD12Q1 (v006) based IGBP land cover classification. Our main focus was the NHL permafrost region because permafrost plays a critical role in the ecology, environment, and society in the NHL. Permafrost, or permanently frozen ground, is defined as ground (soil, sediment, or rock) that remains at or below 0 °C for at least two consecutive years. The occurrence of permafrost is primarily controlled by temperature and has a strong effect on hydrology, soils, and vegetation composition and structure. Based on the categorical permafrost map from the International Permafrost Association58, the permafrost region (excluding permanent snow/ice and barren land), including sporadic (10–50%), discontinuous (50–90%), and continuous ( >90%) permafrost, encompasses about 15.7 × 106 km2, accounts for 57% of the NHL study dominion, and is dominated by tundra (shrubland and grass) and deciduous needleleaf (i.e., larch) forest that is regionally abundant in Siberia. The NHL non-permafrost region covers about 11.9 × 106 km2 and is dominated by mixed and evergreen needleleaf boreal forests (Fig. S1).Atmospheric CO2 inversions (ACIs)ACIs provide regionally-integrated estimates of surface-to-atmosphere net ecosystem CO2 exchange (NEEACI) fluxes by utilizing atmospheric CO2 concentration measurements and atmospheric transport models59. ACIs differ from each other mainly in their underlying atmospheric observations, transport models, spatial and temporal flux resolutions, land surface models used to predict prior fluxes, observation uncertainty and prior error assignment, and inversion methods. We used an ensemble mean of six different ACI products, each providing monthly gridded NEEACI at 1-degree spatial resolution, including Carbon‐Tracker 2019B (2000-2019, CT2019)60, Carbon‐Tracker Europe 2020 (2000–2019, CTE2020)61, Copernicus Atmosphere Monitoring Service (1979–2019, CAMS)62, Jena CarboScope (versions s76_v4.2 1976–2017, and s85_v4.2 1985-2017)63,64, and JAMSTEC (1996–2017)65. The monthly gridded ensemble mean NEEACI at 1-degree spatial resolution was calculated using the available ACIs from 1980-2017. Monthly ACI ensemble mean NEEACI data were summed to seasonal and annual values, and used to calculate the spatial and temporal trends of net CO2 uptake, and to investigate its relationship to climate and environmental controls.Productivity datasetDirect observations of vegetation productivity do not exist at a circumpolar scale. We therefore used two long-term gridded satellite-based estimates of vegetation productivity, including gross primary production (GPP) derived using a light use efficiency (LUE) approach (LUE GPP, 1982–1985)21,66 and satellite observations of Normalized Difference Vegetation Index (NDVI) from the Global Inventory Modeling and Mapping Studies (GIMMS NDVI, 1982–1985)67. LUE GPP (monthly, 0.5° spatial resolution, 1982–2015) is calculated from satellite observations of NDVI from the Advanced Very High-Resolution Radiometer (AVHRR; 1982 to 2015) combined with meteorological data, using the MOD17 LUE approach. LUE GPP has been extensively validated with a global array of eddy-flux tower sites68,69,70 and tends to provide better estimates in ecosystems with greater seasonal variability at high latitudes. Following66,71, we used the ensemble mean of GPP estimates from three of the most commonly used meteorological data sets: National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis; NASA Global Modeling and Assimilation Office (GMAO) Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2); and European Center for Medium-Range Weather Forecasting (ECMWF). GIMMS NDVI (bimonthly, 1/12 spatial resolution, 1982–2015) provides the longest satellite observations of vegetation “greenness”, and is widely used in studies of phenology, productivity, biomass, and disturbance monitoring as it has proven to be an effective surrogate of vegetation photosynthetic activity72.The gridded GPP data were resampled to 1-degree resolution at monthly time scales, to be consistent with NEEACI, and used to test (H1) whether greater temperature sensitivity of vegetation productivity explains the different trends in net CO2 uptake across the NHL. LUE GPP was also used to calculate monthly total ecosystem respiration (TER) as the difference between GPP and NEEACI (i.e., TERresidual =  GPP– NEEACI) from 1982-2015, as global observations of respiration do not exist. The NEEACI, GPP and TERresidual were used as observation-constrained top-down CO2 fluxes to investigate mechanisms underlying the seasonal CO2 dynamics in the structural equation modeling and additional decision tree-based analysis.Eddy Covariance (EC) measurements of bottom-up CO2 fluxesA total of 48 sites with at least three years of data representing the major NHL ecosystems were obtained from the FLUXNET2015 database (Table S1 and Fig. S1). EC measurements provide direct observations of net ecosystem CO2 exchange (NEE) and estimate the GPP and TER flux components of NEE using other climate variables. Daily GPP and TER were estimated as the mean value from both the nighttime partitioning method73 and the light response curve method74. More details on the flux partitioning and gap-filling methods used are provided by75. Daily fluxes were summed into seasonal and annual values and used to compare with trends from ACIs (Fig. S7), to estimate the climate and environmental controls on the CO2 cycle in the pathway analysis (Fig. 5), and to calculate the net CO2 uptake sensitivity to spring temperature (Fig. S14).Ensemble of dynamic global vegetation models (TRENDY simulations)The TRENDY intercomparison project compiles simulations from state-of-the-art dynamic global vegetation models (DGVMs) to evaluate terrestrial energy, water, and net CO2 exchanges76. The DGVMs provide a bottom-up approach to evaluate terrestrial CO2 fluxes (e.g., net biome production [NBP]) and allow deeper insight into the mechanisms driving changes in carbon stocks and fluxes. We used monthly NBP, GPP, and TER (autotrophic + heterotrophic respiration; Ra + Rh) from ten TRENDY v7 DGVMs76, including CABLE-POP, CLM5.0, OCN, ORCHIDEE, ORCHIDEE-CNP, VISIT, DLEM, LPJ, LPJ-GUESS, and LPX. We analyzed the “S3” simulations that include time-varying atmospheric CO2 concentrations, climate, and land use. All simulations were based on climate forcing from the CRU-NCEPv4 climate variables at 6-hour resolution. CO2 flux outputs were summarized monthly at 1-degree spatial resolution from 1980 to 2017. Monthly ensemble mean NBP, GPP, and TER were summed to seasonal and annual values, and then used to compare with observation-constrained ACI top-down CO2 fluxes (Figs. 4 and 5).Satellite data-driven carbon flux estimates (SMAP L4C)We also used a much finer spatio-temporal simulation of carbon fluxes from the NASA Soil Moisture Active Passive (SMAP) mission Level 4 Carbon product (L4C) to quantify the temperature and moisture sensitivity of NHL CO2 exchange77. The SMAP L4C provides global operational daily estimates of NEE and component CO2 fluxes for GPP and TER at 9 km resolution since 2015; whereas, an offline version of the L4C model provides a similar Nature Run (NR) carbon flux record over a longer period (2000-present), but without the influence of SMAP observational inputs. The L4C model has been calibrated against FLUXNET tower CO2 flux measurements and shows favorable performance and accuracy in high latitude regions4,77. In this analysis, daily gridded CO2 fluxes at 9-km resolution from the L4C NR record were summed to seasonal and annual values, and used to calculate the sensitivity of net C uptake in response to spring temperature (Fig. S14).CO2 fluxes in this analysis are defined with respect to the biosphere so that a positive value indicates the biosphere is a net sink of CO2 absorbed from the atmosphere. The different data products described above use different terminology (e.g., NEE, NBP) with slightly different meanings; however, they all provide estimates of net land-atmosphere CO2 exchange78.Climate, tree cover, permafrost, and soil moisture dataMonthly gridded air temperatures at 0.5-degree spatial resolution from 1980 to 2017 were obtained from the Climate Research Unit (CRU TS v4.02) at the University of East Anglia79. Air temperature was summarized at seasonal and annual scales to calculate temperature sensitivities of net CO2 uptake and to investigate the mechanism underlying the seasonal CO2 dynamics.Percent tree cover (%TC) at 0.05-degree spatial resolution was averaged over a 35-year (1982-2016) period using annual %TC layers derived from the Advanced Very High-Resolution Radiometer (AVHRR) (Fig. 1a)42. %TC was binned using 5% TC intervals to assess its relation to net CO2 uptake, or aggregated at a regional scale (e.g., TC  > 50% or TC  90%), discontinuous permafrost (DisconP, 10% < P  90%), discontinuous (DisconP, 10% < P  0.05 indicate a good fitting model), Bentler’s comparative fit index (CFI, where CFI ≈ 1 indicates a good fitting model), and the root mean square error of approximation (RMSEA; where RMSEA ≤ 0.05 and p  > 0.1 indicate a good fitting model). The standardized regression coefficient can be interpreted as the relative influences of exogenous (independent) variables. The R2 indicates the total variation in an endogenous (dependent) variable explained by all exogenous (independent) variables.Direct and legacy effects of temperature on seasonal net CO2 uptakeBecause landscape thawing and snow conditions regulate the onset of vegetation growth and influence the seasonal and annual CO2 cycles in the NHL24,84, we also analyzed the legacy effects of spring (May–Jun) temperature on seasonal net CO2 uptake. We regressed seasonal and annual net CO2 uptake from the site-level EC observations, regional-level ACI ensemble, and the TRENDY NBP ensemble against spring (May-June) air temperature. For EC observations, net CO2 uptake (i.e., NEE) and air temperature were summarized from site-level measurements. For the ACIs and TRENDY ensemble, net CO2 uptake (i.e., NEEACI and NBP) was summarized as regional means from the ACIs and TRENDY ensemble outputs, and air temperature was summarized as regional means from CRU temperature. The slope of the regression line was interpreted as the spring temperature sensitivity of the CO2 cycle. Simple linear regression was used here mainly due to the strong influence of spring temperature on the seasonal and annual CO2 cycle in NHL ecosystems30. Temperature sensitivity (γ: g C m−2 day−1 K−1) is the change in net CO2 flux (g C m−2 day−1) in response to a 1-degree temperature change. The sensitivity of net CO2 uptake to warm spring anomalies was calculated for different seasons (EGS, LGS, and annual) and regions (i.e., permafrost and non-permafrost), and the T-test was used to test for the difference in γ among different regions, seasons, and datasets. Similarly, direct effects of temperature on net CO2 uptake were calculated using the same season data (Fig. S14).Observationally-constrained estimates (EC and ACIs) showed that the sensitivity of net CO2 uptake in the EGS to spring temperature is positive (γ  > 0) and not statistically different (p  > 0.05) between permafrost and non-permafrost regions (({gamma }_{{ACI}}^{{np}})=0.125 ± 0.020 gC m−2 d−1 K−1; ({gamma }_{{EC}}^{{np}}) = 0.052 ± 0.013 gC m−2 d−1 K−1). In contrast, the sensitivity of net CO2 uptake in LGS to spring temperature is negative (γ  More