Climate change influences cryospheric conditions in the boreal zone, which in turn affect carbon uptake and release by vegetation. Knowledge about these interactions is important to enhance understanding of how the carbon cycle in this region responds to climate change and to accurately assess its impact on the carbon balance. Based on a 10-year time series of CO2 flux measurements from five boreal evergreen forests in Finland and Canada, we investigated the relationship between yearly anomalies in snow melt, soil freeze and thaw and anomalies in the start- and end-of-season dates calculated from gross primary production and terrestrial ecosystem respiration. We used both in-situ and satellite-based information. Soil freeze and thaw, and snow melt dates were estimated from the ESA SMOS Level 3 Soil Freeze and Thaw, and the ESA Climate Change Initiative snow cover fraction on the ground data, respectively. At the investigated forest sites, we observed a time lag between the seasonal cycle of photosynthesis and respiration. Year-to-year changes in the timing of recovery of photosynthesis and ecosystem respiration were well correlated with year-to-year changes in snow melt and soil thaw dates, both from in-situ and satellite observations. Earlier snow melt and soil thaw were associated with higher annual carbon uptake. This study highlights linkages between carbon cycle and cryospheric variables. Satellite-based information on snow melt, soil freeze and thaw can support spatial evaluations of carbon balance model results, the upscaling of carbon fluxes and are important variables in ecological studies in northern regions.

Fires in the Amazon are of great concern because they threaten the integrity of the tropical forest biome, the carbon cycle, and air quality. Fire emissions depend on the burning behaviour of vegetation biomass, woody debris, and litter. However, the effects of fuels on the combustion process and on the composition of fire emissions are simplified in current fire emission inventories and models. Several new fire emission approaches have recently been developed to better quantify fire emissions by either making use the improved spatial resolution of modern satellite observations or by developing new modelling approaches. Here we compare several current and novel approaches to quantify fuel consumption and fire emissions for the Amazon and Cerrado for the fire season in 2020. The approaches include the widely used GFAS, a top-down approach based on Sentinel-5p observations (KNMI.S5p), a bottom-up approach based on active fire observations from VIIRS (GFA.S4F), two bottom-up approaches based on MODIS burned area data (500-m version of GFED, REFIT.AC), a data-model fusion approach with dynamic emission factors that integrates several Earth observation products (TUD.S4F), and three dynamic global vegetation models in diagnostic mode with prescribed burned area. The different approaches to estimate fire emission show that forest and deforestation fires dominate the regional total fire emissions. However, large differences exist in the very high emissions of individual fires that mainly contribute to the regional total fire emissions. We found a higher agreement in estimated CO and NOx emissions between approaches for savannah fires (normalised RMSE < 20%) than for forest and deforestation fires (nRMSE 30%). We estimate that only 10% of all fire events contribute between 85% and 97% of the regional total fire emissions. By using the TUD.S4F data-model fusion approach with dynamic emission factors, we show that most fire CO emissions originate from the burning of woody debris, which burns with low combustion efficiency and hence has higher emission factors for CO. Comparisons with regional field-based investigations show, however, large differences in estimates of surface fuel loads and fuel consumption. Our results demonstrate the advantage of exploring several complementary fire emission approaches to better understand the underlying processes and to account for regional to global fire emissions and their uncertainties.

The Amazon forest is fire sensitive, but, where fires were uncommon as a natural disturbance, deforestation and drought are accelerating fire occurrences, which threaten the integrity of the tropical forest, the carbon cycle and air quality. Fire emissions depend on fuel amount and type, moisture conditions and burning behaviour. Higher-resolution satellite data have helped more accurately map global burnt areas; however, the effects of fuels on the combustion process and on the composition of fire emissions remain uncertain in current fire emissions inventories. By using multiple Earth observation-based approaches, here we show that total fire emissions in the Amazon and Cerrado biomes are dominated by smouldering combustion of woody debris. The representation of woody debris and surface litter presents a critical uncertainty in fire emissions inventories and global vegetation models. For the fire season 1 August to 31 October 2020, for which all approaches are available, we found 372^605_277 Tg (median and range across approaches) of dry matter burnt, corresponding to carbon monoxide emissions of 39.1^59_27 Tg . Our results emphasize how Earth observation approaches for fuel and fire dynamics and of atmospheric trace gases reduce uncertainties of fire emission estimates. The findings enable diagnosing the representation of fuels, wildfire combustion and its effects on atmospheric composition and the carbon cycle in global vegetation–fire models. Fire emissions in the Amazon and Cerrado biomes are mainly produced from smouldering combustion of woody debris, according to observationally constrained fire emissions inventories.

Fire substantially influences and modulates the global carbon cycle through numerous processes, interactions, and feedbacks. Fires are also strongly intertwined with human activities; people act both as drivers of change through ignitions, suppression, land-cover change, prescribed burning, and climate change, and are affected in return by changes in fire regimes. Despite fire’s many complex interactions throughout the Earth System, it is often viewed only as a destructive process, and one that solely acts as a source of atmospheric carbon. In terms of fire’s carbon budget, the release of carbon only represents the very initial stages of the process, missing the drivers and complex ways in which fire shapes plant species evolution and ecosystem trajectories, nutrient cycling and redistribution, carbon allocation, deposition and sequestration over different spatiotemporal scales. Therefore, there is a clear need to fully understand the role of fire in the Earth System holistically. However, different aspects of fire’s role in the carbon cycle are often studied by different communities and disciplines, hindering this much-needed integrated understanding. Through the Fire Learning AcRoss the Earth Systems (FLARE) workshop (September 2023) we brought together fire scientists across multiple disciplines to facilitate transdisciplinary discussion. We propose that the visualization of fire processes as carbon colours across the Earth System can be a thematic tool for unifying disciplines. It explores all aspects of fire and smoke implications for living systems and opens questions about fire’s role in carbon budgets, afforestation, and climate change and related mitigation strategies. We also identified several scientific challenges for the community where, by working together, we can address some fundamental questions for fire’s role in the carbon cycle, such as: What is the contribution of fire and of individual fire events to the global carbon cycle? How do changes in fire regimes influence ecosystem stability across different timescales? How do future changes in fire regimes influence global climate, allowable emissions and carbon budgets, and temperature mitigation ambitions? In this presentation, we explore how we can bring a more interdisciplinary approach to fire science to address these fundamental questions.

In September 2023, the Fire Learning AcRoss the Earth Systems (FLARE) workshop brought together fire scientists across a wide range of disciplines, including physical and social scientists and representatives of fire-prone communities, with the aim to facilitate a transdisciplinary discussion. The FLARE community identified characterizing “fire and extreme events” as a research priority. In recent years, there has been a rise in extreme weather events worldwide. Both in science and in the media, the word “extreme” is increasingly used to describe the impact of natural phenomena on ecosystems, human health, the carbon cycle, and economies. However, the severity associated with recent changes in fire activity is not well defined. Assessing the cause(s) and consequences of a fire event on a global scale is complex, this leads to different definitions and assessment techniques/methods being used in the range of disciplines that study fire, including ecology, biology, hydrology, atmospheric science, marine science, Earth science, or public health. Additionally, it is hard to disentangle human land management and climate change induced changes in fire regimes. Using examples from the 2023 Boreal fires, this presentation discusses future directions for defining extreme fires. Fires are also part of the broader interconnected Earth System and influenced by droughts, heat waves, and altered landscapes. In turn, post-fire effects such as erosion, landslides, and floods create cascade events that impact both human societies and natural ecosystems. We discuss this broader view of including fire extremes as part of compound extreme events in order to fully assess their impact. We finish by providing recommendations for the fire science community to tackle this challenge. Some of which may include more proactive modeling, observation and communication tools aimed at providing relevant and timely information. https://futureearth.org/2023/12/13/reflections-from-the-fire-science-learning-across-the-earth-system-flare-workshop/

Estimates of vegetation biophysical variables, including leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FAPAR), are essential for effective agricultural and forest monitoring/management. They also represent a crucial input into crop yield, carbon exchange, and weather and climate system models. Exploiting optical Earth Observation (EO), retrieval algorithms now provide routine decametric (10 m to 100 m) estimates of LAI and FAPAR, but are subject to known biases due to assumptions embedded within the radiative transfer models used in their training. To overcome these limitations, retrieval approaches might be based on real EO data and contemporaneous ground reference observations. Historically, however, such ground reference observations have been limited in quantity and suffered from inconsistencies and unquantified measurement uncertainties.Recently, advances in novel automated field instrumentation, standardised processing, and routine data collection by environmental monitoring networks (including the National Ecological Observatory Network (NEON), Integrated Carbon Observation System (ICOS), and Terrestrial Ecosystem Research Network (TERN), amongst others) have led to substantial improvements in ground reference data spatiotemporal coverage and consistency. In parallel, cutting-edge machine learning approaches such as Gaussian processes have dramatically reduced the number of required training samples (i.e. thousands as opposed to hundreds of thousands). These approaches also enable the explicit treatment of measurement uncertainties. Through work under the Fiducial Reference Measurements for Vegetation (FRM4VEG) programme and other similar initiatives, methods to quantify ground reference measurement uncertainties in a traceable manner have recently become available.As a result of these advances, for the first time, the development of operational retrieval approaches based on real observations is a realistic possibility. Funded by the European Space Agency’s Living Planet Fellowship programme, the Ground Reference Observations Underlying Novel Decametric Vegetation Data Products from Earth Observation (GROUNDED EO) project is adopting such a data-driven strategy. To date, work has primarily focused on developing a harmonised ground reference database. Raw digital hemispherical photography (DHP) and digital cover photography (DCP) has been obtained from 47 NEON sites in the United States, 20 ICOS sites in Europe, and 10 TERN sites in Australia. This have been processed to derive plant area index (PAI) and the fraction of intercepted photosynthetically active radiation (FIPAR), with associated uncertainties.To make use of the harmonised ground reference database for biophysical variable retrieval, a matchup database containing contemporaneous Sentinel-2 Level 2A surface reflectance observations is being assembled. Because the Collection 1 reprocessing is not yet complete, Level 2A products prior to December 2018 (or March 2018 in Europe) are not available, severely limiting the number of matchups. To address this, work is underway to process all associated Level 1C products acquired over GROUNDED EO sites to Level 2A using Sen2Cor. Once complete, work will commence on the development of a data-driven biophysical processor. Gaussian processes, which have already proven efficient for radiative transfer model based biophysical variable retrieval, will be utilised for this. Active learning will be adopted to select only the most informative training samples, reducing the size of the training dataset and increasing computational efficiency and accuracy.

&lt;p&gt;Photosynthesis is a major driver of terrestrial ecosystem dynamics. Unfortunately, gross primary productivity (GPP), or the rate at which solar energy is captured and stored into sugar molecules during photosynthesis, cannot be directly measured from remote sensing (RS) signals. Several RS signals related to vegetation pigments and to canopy structure can, however, serve as proxies for GPP. They can further be combined with different types and degrees of modelling to derive spatio-temporal estimations of GPP. Different strategies exist to do so, which often vary with respect to how much they depend on an in-situ reference for GPP, the gold standard being those derived from eddy covariance (EC) measurements at flux tower sites.&lt;/p&gt; &lt;p&gt;Here we investigate several such strategies with a specific goal: to explore the potential contribution of Sentinel satellites to improve GPP estimation. The Sentinel fleet is maintained by the European Union&amp;#8217;s Copernicus programme, thereby guaranteeing a certain longevity and enabling the establishment of operational services that do not depend on single satellite missions. The main RS signals we consider are: the OLCI global vegetation index (OGVI) and OLCI terrestrial chlorophyll index (OTCI) from the Sentinel-3 OLCI instrument; daytime and night-time land surface temperature (LST) from Sentinel-3 SLSTR; and sun-induced chlorophyll fluorescence (SIF) from TROPOMI on-board of Sentinel-5-P. We further use time series of Sentinel-2 data to quantify the spatial homogeneity within the observational footprints of these coarser spatial resolution products in order to ensure a proper comparison to flux-tower data. The whole exercise is part of the Sen4GPP project funded by the European Space Agency (ESA).&lt;/p&gt; &lt;p&gt;The three strategies we explore to derive GPP are: (1) empirical SIF-based estimation of GPP, including a version involving spatial downscaling to reach a finer resolution of SIF; (2) deterministic modelling based on a quantum yield light use efficiency (LUE) model calibrated on EC flux towers; and (3) purely data-driven machine learning (ML) based on EC measurements at flux towers using dedicated 10-fold cross-validation using the FLUXCOM-X framework. The cross-comparison is done for independent flux tower sites over Europe based on the Warm Winter 2020 database, covering the recent past (2018-2020) when TROPOMI SIF observations are available.&lt;/p&gt; &lt;p&gt;The results indicate that the ML approach clearly outperforms the process-based LUE approach, which itself performs better than SIF. However, this order also reflects a decreasing reliance in flux tower data and possibly increasing capacity to extrapolate to situations not present in the learning dataset. The results further indicate that the ML approach using Sentinel data can perform better than a baseline using MODIS data alone, probably due to the inclusion of SIF information. Results also illustrate how ensuring the spatial consistency between grid and tower does improve performance, strengthening the rational for spatially downscaling coarse RS signals such as SIF. Overall, these encouraging results bode well for the potential use of Sentinel data to improve our current capacity to monitor biogeochemical process at global scale.&lt;/p&gt;

Glaciers are currently the largest contributor to sea level rise after ocean thermal expansion, contributing ∼ 30 % to the sea level budget. Global monitoring of these regions remains a challenging task since global estimates rely on a variety of observations and models to achieve the required spatial and temporal coverage, and significant differences remain between current estimates. Here we report the first application of a novel approach to retrieve spatially resolved elevation and mass change from radar altimetry over entire mountain glaciers areas. We apply interferometric swath altimetry to CryoSat-2 data acquired between 2010 and 2019 over High Mountain Asia (HMA) and in the Gulf of Alaska (GoA). In addition, we exploit CryoSat's monthly temporal repeat to reveal seasonal and multiannual variation in rates of glaciers' thinning at unprecedented spatial detail. We find that during this period, HMA and GoA have lost an average of −28.0 ± 3.0 Gt yr−1 (−0.29 ± 0.03 m w.e. yr−1) and −76.3 ± 5.7 Gt yr−1 (−0.89 ± 0.07 m w.e. yr−1), respectively, corresponding to a contribution to sea level rise of 0.078 ± 0.008 mm yr−1 (0.051 ± 0.006 mm yr−1 from exorheic basins) and 0.211 ± 0.016 mm yr−1. The cumulative loss during the 9-year period is equivalent to 4.2 % and 4.3 % of the ice volume, respectively, for HMA and GoA. Glacier thinning is ubiquitous except for in the Karakoram–Kunlun region, which experiences stable or slightly positive mass balance. In the GoA region, the intensity of thinning varies spatially and temporally, with acceleration of mass loss from −0.06 ± 0.33 to −1.1 ± 0.06 m yr−1 from 2013, which correlates with the strength of the Pacific Decadal Oscillation. In HMA ice loss is sustained until 2015–2016, with a slight decrease in mass loss from 2016, with some evidence of mass gain locally from 2016–2017 onwards.

Glaciers and ice caps are currently the largest non-steric contributor to sea level rise, contributing ~30 % to sea level budget. Global monitoring of these regions remains a challenging task since global estimates rely on a variety of observations and models to achieve the required spatial and temporal coverage, and significant differences remain between current estimates. Here we report the first application of a novel approach to retrieve spatially-resolved elevation and mass change from Radar Altimetry over entire mountain glaciers areas. We apply interferometric swath altimetry to CryoSat-2 data acquired between 2010 and 2019 over High Mountain Asia (HMA) and in the Gulf of Alaska (GoA). In addition, we extract monthly time series of elevation change, exploiting CryoSat's high temporal repeat, to reveal seasonal and multiannual variation in rates of glaciers' thinning at unprecedented spatial detail. We find that during this period, HMA and GoA have lost an average of −27.9 ± 2.4 Gt yr−1 (−0.29 ± 0.03 m w.e. yr−1) and −76.3 ± 5.6 Gt yr−1 (−0.89 ± 0.07 m w.e. yr−1) respectively, corresponding to a contribution to sea level rise of 0.048 ± 0.004 mm yr−1 and 0.217 ± 0.015 mm yr−1. Glacier thinning is ubiquitous except for the Karakoram-Kunlun region experiencing stable or slightly positive mass balance. In the GoA region the intensity of thinning varies spatially and temporally and correlates with the strength of the Pacific Decadal Oscillation. In HMA we observe sustained multiannual trends until 2015-6, and decreased loss or even mass gain from 2016-17 onwards.

In Alpine regions changes in seasonal climatic parameters, such as temperature, rainfall, and snow amount have already been observed. Specifically, in the South Tyrol area, meteorological observations indicate that temperatures are increasing and the number of snow days has generally diminished over time with perennial snow line now observed at higher elevations. Changes in rainfall have also been observed with more events associated with higher temperatures in the summer season. Natural hazards - mainly debris and mud flows, landslides, avalanches, rock falls, and (flash) floods - that affect this area every year, damaging population and infrastructures, are either weather or cryosphere-related. While these events have been recorded sporadically since the beginning of the 20th century, a systematic approach of their inventory has been done by local authorities since the 1990s. So far, Earth observation data has not been exploited to complete or complement existing inventories nor have they been used to investigate the influence of climate perturbation on potentially dangerous natural phenomena. The research presented here thus has three objectives: (i) analyse long time series of climate data and hazard occurrence in South Tyrol to examine if these records exhibit a coherent response of hazards to changes in climate; (ii) measure the spatio-temporal evolution of climatic and natural hazard events recorded, and (iii) explore potential relations between meteorological conditions and the hazard occurrence. In this context, in-situ and satellite-based climate data are exploited to study natural hazard triggers while the potential of Earth observation data is evaluated as a complement to the existing historical records of natural hazards. Specifically, Copernicus Sentinel-1 images are used to detect the spatio-temporal distribution of slow earth surface deformations and the results used for checking the completeness of the actual slow-moving landslide inventories. Hazard-related changes in the South Tyrolian landscape have also been analysed in relation to particular meteorological events at a regional scale, assessing trends and anomalies. Results show that: (i) satellite data are very useful to complement the existing natural hazard inventories; (ii) in-situ and satellite-based climate records show similar patterns but differ due to regional versus local variability; (iii) even in a data-rich region such as the analysed area, the overall response of natural hazard occurrence, magnitude, and frequency to change in climate variables is difficult to decipher due to the presence of multiple triggers and locally driven ground responses. However, an increase in the average annual duration of rainfall events and debris flow occurrence can be observed.

Swath mode processing of CryoSat-2 Synthetic Aperture Radar Interferometric (SARIn) mode has been used to monitor elevation of areas with complex topography such as over ice sheet and ice cap margins. Swath processing relies on an accurate measure of the angle of arrival of the measured echo and, therefore, requires custom strategies in order to resolve the ambiguous phase measurement. In mountainous regions of complex terrain, it may be necessary to apply different phase ambiguities across a waveform record when returns come from different scatters distributed perpendicularly to the CryoSat-2 ground tracks. In this letter, we present modifications to the conventional swath processing method whereby a multisurface retracker is first applied to the record in order to identify potential different scattering surfaces. Phase ambiguity is then independently resolved for each of these subsurfaces. The improvements with this new method over the Karakoram glaciers are a 10% increase in the number of measurements with improvements of almost 50% for individual glaciers and a reduction in the median absolute deviation of the elevations from 20.18 to 14.69 m.

Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI.

This paper presents a new global burned area (BA) product, generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) red (R) and near-infrared (NIR) reflectances and thermal anomaly data, thus providing the highest spatial resolution (approx. 250 m) among the existing global BA datasets. The product includes the full times series (2001-2016) of the Terra-MODIS archive. The BA detection algorithm was based on monthly composites of daily images, using temporal and spatial distance to active fires. The algorithm has two steps, the first one aiming to reduce commission errors by selecting the most clearly burned pixels (seeds), and the second one targeting to reduce omission errors by applying contextual analysis around the seed pixels. This product was developed within the European Space Agency's (ESA) Climate Change Initiative (CCI) programme, under the Fire Disturbance project (Fire_cci). The final output includes two types of BA files: monthly full-resolution continental tiles and biweekly global grid files at a degraded resolution of 0.25 degrees. Each set of products includes several auxiliary variables that were defined by the climate users to facilitate the ingestion of the product into global dynamic vegetation and atmospheric emission models. Average annual burned area from this product was 3.81 Mkm(2), with maximum burning in 2011 (4.1 Mkm(2)) and minimum in 2013 (3.24 Mkm(2)) The validation was based on a stratified random sample of 1200 pairs of Landsat images, covering the whole globe from 2003 to 2014. The validation indicates an overall accuracy of 0.9972, with much higher errors for the burned than the unburned category (global omission error of BA was estimated as 0.7090 and global commission as 0.5123). These error values are similar to other global BA products, but slightly higher than the NASA BA product (named MCD64A1, which is produced at 500 m resolution). However, commission and omission errors are better compensated in our product, with a tendency towards BA underestimation (relative bias -0.4033), as most existing global BA products. To understand the value of this product in detecting small fire patches (< 100 ha), an additional validation sample of 52 Sentinel-2 scenes was generated specifically over Africa. Analysis of these results indicates a better detection accuracy of this product for small fire patches (< 100 ha) than the equivalent 500 m MCD64A1 product, although both have high errors for these small fires. Examples of potential applications of this dataset to fire modelling based on burned patches analysis are included in this paper. The datasets are freely downloadable from the Fire_cci website (https://www.esa-fire-cci.org/, last access: 10 November 2018) and their repositories (pixel at full resolution: https://doi.org/cpk7, and grid: https://doi.org/gcx9gf).

The Earth System behaves as a highly coupled system comprising physical, chemical, biological, and anthropogenic components and processes with complex interactions and feedbacks between them. Climate change is arguably the greatest challenge to balance in the Earth system. The Paris Agreement (UNFCCC, 2016) recognised the need to reduce the risks from and impacts of climate change and called for the increase in the global average temperature to be held well below 2 °C above pre-industrial levels, with the ideal aim being to limit it to 1.5 °C. The UN Framework Convention on Climate Change (UNFCCC) agreement is based on the evidence for, and likely causes of, climate change synthesised by the Intergovernmental Panel on Climate Change (IPCC) and is supported, for climate, by the Global Climate Observing System (GCOS). GCOS has defined a set of Essential Climate Variables (ECV), established the requirements for their systematic observation, and the development of data archives, needed to support the study the climate system. The Climate Change Initiative (CCI), represents the contribution by the European Space Agency (ESA) to GCOS. CCI is a programme designed to bring together European expertise in Earth Observation with that from the climate research community to address those ECV that can be generated using satellite observations. Specifically the objective is ‘to realise the full potential of the long-term global Earth Observation archives that ESA together with its Member States have established over the last thirty years, as a significant and timely contribution to the ECV databases required by the United Nations Framework Convention on Climate Change (UNFCCC)’. In doing so the intended legacy of the programme is to put in place mechanisms capable of providing long term, fully traceable, and transparent access to its records. This paper provides an overview of the CCI Programme and highlights a few of its achievements to date.

Fire plays an important role in terrestrial ecosystems by regulating biogeochemistry, biogeography, and energy budgets, yet despite the importance of fire as an integral ecosystem process, significant advances remain to improve its prognostic representation in carbon cycle models. To recommend and to help prioritize model improvements, this study investigates the sensitivity of a coupled global biogeography and biogeochemistry model, LPJ, to observed burned area measured by three independent satellite‐derived products, GFED v3.1, L3JRC, and GlobCarbon. Model variables are compared with benchmarks that include pantropical aboveground biomass, global tree cover, and CO 2 and CO trace gas concentrations. Depending on prescribed burned area product, global aboveground carbon stocks varied by 300 Pg C, and woody cover ranged from 50 to 73 Mkm 2 . Tree cover and biomass were both reduced linearly with increasing burned area, i.e., at regional scales, a 10% reduction in tree cover per 1000 km 2 , and 0.04‐to‐0.40 Mg C reduction per 1000 km 2 . In boreal regions, satellite burned area improved simulated tree cover and biomass distributions, but in savanna regions, model‐data correlations decreased. Global net biome production was relatively insensitive to burned area, and the long‐term land carbon sink was robust, ~2.5 Pg C yr −1 , suggesting that feedbacks from ecosystem respiration compensated for reductions in fuel consumption via fire. CO 2 transport provided further evidence that heterotrophic respiration compensated any emission reductions in the absence of fire, with minor differences in modeled CO 2 fluxes among burned area products. CO was a more sensitive indicator for evaluating fire emissions, with MODIS‐GFED burned area producing CO concentrations largely in agreement with independent observations in high latitudes. This study illustrates how ensembles of burned area data sets can be used to diagnose model structures and parameters for further improvement and also highlights the importance in considering uncertainties and variability in observed burned area data products for model applications.

The OLIVE (On Line Interactive Validation Exercise) platform is dedicated to the validation of global biophysical products such as LAI (Leaf Area Index) and FAPAR (Fraction of Absorbed Photosynthetically Active Radiation). It was developed under the framework of the CEOS (Committee on Earth Observation Satellites) Land Product Validation (LPV) sub-group. OLIVE has three main objectives: (i) to provide a consistent and centralized information on the definition of the biophysical variables, as well as a description of the main available products and their performances (ii) to provide transparency and traceability by an online validation procedure compliant with the CEOS LPV and QA4EO (Quality Assurance for Earth Observation) recommendations (iii) and finally, to provide a tool to benchmark new products, update product validation results and host new ground measurement sites for accuracy assessment. The functionalities and algorithms of OLIVE are described to provide full transparency of its procedures to the community. The validation process and typical results are illustrated for three FAPAR products: GEOV1 (VEGETATION sensor), MGVIo (MERIS sensor) and MODIS collection 5 FPAR. OLIVE is available on the European Space Agency CAL/VAL portal), including full documentation, validation exercise results, and product extracts.