Compensatory water effects link yearly global land CO2 sink changes to temperature.

Large interannual variations in the measured growth rate of atmospheric carbon dioxide (CO2) originate primarily from fluctuations in carbon uptake by land ecosystems. It remains uncertain, however, to what extent temperature and water availability control the carbon balance of land ecosystems across spatial and temporal scales. Here we use empirical models based on eddy covariance data and process-based models to investigate the effect of changes in temperature and water availability on gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) at local and global scales. We find that water availability is the dominant driver of the local interannual variability in GPP and TER. To a lesser extent this is true also for NEE at the local scale, but when integrated globally, temporal NEE variability is mostly driven by temperature fluctuations. We suggest that this apparent paradox can be explained by two compensatory water effects. Temporal water-driven GPP and TER variations compensate locally, dampening water-driven NEE variability. Spatial water availability anomalies also compensate, leaving a dominant temperature signal in the year-to-year fluctuations of the land carbon sink. These findings help to reconcile seemingly contradictory reports regarding the importance of temperature and water in controlling the interannual variability of the terrestrial carbon balance. Our study indicates that spatial climate covariation drives the global carbon cycle response.

Monitoring tropical forests under a functional perspective with satellite-based vegetation optical depth.

Monitoring ecosystem functions in forests is a priority in a climate change scenario, as climate-induced events may initially alter the functions more than slow-changing attributes, such as biomass. The ecosystem functional properties (EFPs) are quantities that characterize key ecosystem processes. They can be derived by point observations of gas and energy exchanges between the ecosystems and the atmosphere that are collected globally at FLUXNET flux tower sites and upscaled at ecosystem level. The properties here considered describe the ability of ecosystems to optimize the use of resources for carbon uptake. They represent functional forest information, are dependent on environmental drivers, linked to leaf traits and forest structure, and influenced by climate change effects. The ability of vegetation optical depth (VOD) to provide forest functional information is investigated using 2011-2014 satellite data collected by the Soil Moisture and Ocean Salinity mission and using the EFPs as reference dataset. Tropical forests in Africa and South America were analyzed, also according to ecological homogeneous units. VOD jointly with water deficit information explained 93% and 87% of the yearly variability in both flux upscaled maximum gross primary productivity and light use efficiency functional properties, in Africa and South America forests respectively. Maps of the retrieved properties evidenced changes in forest functional responses linked to anomalous climate-induced events during the study period. The findings indicate that VOD can support the flux upscaling process in the tropical range, affected by high uncertainty, and the detection of forest anomalous functional responses. Preliminary temporal analysis of VOD and EFP signals showed fine-grained variability in periodicity, in signal dephasing, and in the strength of the relationships. In selected drier forest types, these satellite data could also support the monitoring of functional dynamics.

Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks.

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO2 fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co-acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data-driven approach based on combined neural networks (NNC-part ). NNC-part incorporates process knowledge by introducing a photosynthetic response based on the light-use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R2  > .94) and RECO (R2  > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NNC-part performed more realistic than the traditional methods for predicting additional patterns of gross CO2 fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO2 fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NNC-part is a valid data-driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.

Intercomparison of methods to estimate gross primary production based on CO2 and COS flux measurements.

Separating the components of ecosystem-scale carbon exchange is crucial in order to develop better models and future predictions of the terrestrial carbon cycle. However, there are several uncertainties and unknowns related to current photosynthesis estimates. In this study, we evaluate four different methods for estimating photosynthesis at a boreal forest at the ecosystem scale, of which two are based on carbon dioxide (CO2) flux measurements and two on carbonyl sulfide (COS) flux measurements. The CO2-based methods use traditional flux partitioning and artificial neural networks to separate the net CO2 flux into respiration and photosynthesis. The COS-based methods make use of a unique 5-year COS flux data set and involve two different approaches to determine the leaf-scale relative uptake ratio of COS and CO2 (LRU), of which one (LRUCAP) was developed in this study. LRUCAP was based on a previously tested stomatal optimization theory (CAP), while LRUPAR was based on an empirical relation to measured radiation. For the measurement period 2013-2017, the artificial neural network method gave a GPP estimate very close to that of traditional flux partitioning at all timescales. On average, the COS-based methods gave higher GPP estimates than the CO2-based estimates on daily (23% and 7% higher, using LRUPAR and LRUCAP, respectively) and monthly scales (20% and 3% higher), as well as a higher cumulative sum over 3 months in all years (on average 25% and 3% higher). LRUCAP was higher than LRU estimated from chamber measurements at high radiation, leading to underestimation of midday GPP relative to other GPP methods. In general, however, use of LRUCAP gave closer agreement with CO2-based estimates of GPP than use of LRUPAR. When extended to other sites, LRUCAP may be more robust than LRUPAR because it is based on a physiological model whose parameters can be estimated from simple measurements or obtained from the literature. In contrast, the empirical radiation relation in LRUPAR may be more site-specific. However, this requires further testing at other measurement sites.

The FLUXCOM ensemble of global land-atmosphere energy fluxes.

Although a key driver of Earth's climate system, global land-atmosphere energy fluxes are poorly constrained. Here we use machine learning to merge energy flux measurements from FLUXNET eddy covariance towers with remote sensing and meteorological data to estimate global gridded net radiation, latent and sensible heat and their uncertainties. The resulting FLUXCOM database comprises 147 products in two setups: (1) 0.0833° resolution using MODIS remote sensing data (RS) and (2) 0.5° resolution using remote sensing and meteorological data (RS + METEO). Within each setup we use a full factorial design across machine learning methods, forcing datasets and energy balance closure corrections. For RS and RS + METEO setups respectively, we estimate 2001-2013 global (±1 s.d.) net radiation as 75.49 ± 1.39 W m-2 and 77.52 ± 2.43 W m-2, sensible heat as 32.39 ± 4.17 W m-2 and 35.58 ± 4.75 W m-2, and latent heat flux as 39.14 ± 6.60 W m-2 and 39.49 ± 4.51 W m-2 (as evapotranspiration, 75.6 ± 9.8 × 103 km3 yr-1 and 76 ± 6.8 × 103 km3 yr-1). FLUXCOM products are suitable to quantify global land-atmosphere interactions and benchmark land surface model simulations.