ALBERT

Description: Land models, which have been developed by the modeling community in the past two decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure and evaluate performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land models. The framework includes (1) targeted aspects of model performance to be evaluated; (2) a set of benchmarks as defined references to test model performance; (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies; and (4) model improvement. Component 4 may or may not be involved in a benchmark analysis but is an ultimate goal of general modeling research. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and the land-surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics across timescales in response to both weather and climate change. Benchmarks that are used to evaluate models generally consist of direct observations, data-model products, and data-derived patterns and relationships. Metrics of measuring mismatches between models and benchmarks may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data-model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance for future improvement. Iterations between model evaluation and improvement via benchmarking shall demonstrate progress of land modeling and help establish confidence in land models for their predictions of future states of ecosystems and climate.

Description: Peatlands are a major terrestrial carbon store and a persistent natural carbon sink during the Holocene, but there is considerable uncertainty over the fate of peatland carbon in a changing climate. It is generally assumed that higher temperatures will increase peat decay, causing a positive feedback to climate warming and contributing to the global positive carbon cycle feedback. Here we use a new extensive database of peat profiles across northern high latitudes to examine spatial and temporal patterns of carbon accumulation over the past millennium. Opposite to expectations, our results indicate a small negative carbon cycle feedback from past changes in the long-term accumulation rates of northern peatlands. Total carbon accumulated over the last 1000 yr is linearly related to contemporary growing season length and photosynthetically active radiation, suggesting that variability in net primary productivity is more important than decomposition in determining long-term carbon accumulation. Furthermore, northern peatland carbon sequestration rate declines over the climate transition from the Medieval Climate Anomaly (MCA) to the Little Ice Age (LIA), probably because of lower LIA temperatures combined with increased cloudiness suppressing net primary productivity. Other factors including changing moisture status, peatland distribution, fire, nitrogen deposition, permafrost thaw and methane emissions will also influence future peatland carbon cycle feedbacks, but our data suggest that the carbon sequestration rate could increase over many areas of northern peatlands.

Description: Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data–model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models to improve their prediction performance skills.

Description: Globally, terrestrial ecosystems have absorbed about 30% of anthropogenic greenhouse gas emissions over the period 2000–2007 and inter-hemispheric gradients indicate that a significant fraction of terrestrial carbon sequestration must be north of the Equator. We present a compilation of the CO2, CO, CH4 and N2O balances of Europe following a dual constraint approach in which (1) a land-based balance derived mainly from ecosystem carbon inventories and (2) a land-based balance derived from flux measurements are compared to (3) the atmospheric data-based balance derived from inversions constrained by measurements of atmospheric GHG (greenhouse gas) concentrations. Good agreement between the GHG balances based on fluxes (1294 ± 545 Tg C in CO2-eq yr−1), inventories (1299 ± 200 Tg C in CO2-eq yr−1) and inversions (1210 ± 405 Tg C in CO2-eq yr−1) increases our confidence that the processes underlying the European GHG budget are well understood and reasonably sampled. However, the uncertainty remains large and largely lacks formal estimates. Given that European net land to atmosphere exchanges are determined by a few dominant fluxes, the uncertainty of these key components needs to be formally estimated before efforts could be made to reduce the overall uncertainty. The net land-to-atmosphere flux is a net source for CO2, CO, CH4 and N2O, because the anthropogenic emissions by far exceed the biogenic sink strength. The dual-constraint approach confirmed that the European biogenic sink removes as much as 205 ± 72 Tg C yr−1 from fossil fuel burning from the atmosphere. However, This C is being sequestered in both terrestrial and inland aquatic ecosystems. If the C-cost for ecosystem management is taken into account, the net uptake of ecosystems is estimated to decrease by 45% but still indicates substantial C-sequestration. However, when the balance is extended from CO2 towards the main GHGs, C-uptake by terrestrial and aquatic ecosystems is offset by emissions of non-CO2 GHGs. As such, the European ecosystems are unlikely to contribute to mitigating the effects of climate change.

Description: High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or Light Detection and Ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (〉40 %) of the Colombian Amazon – a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon mapping samples had 14.6 % uncertainty at 1 ha resolution, and regional maps based on stratification and regression approaches had 25.6 % and 29.6 % uncertainty, respectively, in any given hectare. High-resolution approaches with reported local-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision-makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.

Description: Globally, terrestrial ecosystems have absorbed about 30% of anthropogenic emissions over the period 20007–2007 and inter-hemispheric gradients indicate that a significant fraction of terrestrial carbon sequestration must be north of the Equator. We present a compilation of the CO2, CO, CH4 and N2O balance of Europe following a dual constraint approach in which (1) a land-based balance derived mainly from ecosystem carbon inventories and (2) a land-based balance derived from flux measurements are confronted with (3) the atmospheric-based balance derived from inversion informed by measurements of atmospheric GHG concentrations. Good agreement between the GHG balances based on fluxes (1249 ± 545 Tg C in CO2-eq y−1), inventories (1299 ± 200 Tg C in CO2-eq y−1) and inversions (1210 ± 405 Tg C in CO2-eq y−1) increases our confidence that current European GHG balances are accurate. However, the uncertainty remains large and largely lacks formal estimates. Given that European net land-atmosphere balances are determined by a few dominant fluxes, the uncertainty of these key components needs to be formally estimated before efforts could be made to reduce the overall uncertainty. The dual-constraint approach confirmed that the European land surface, including inland waters and urban areas, is a net source for CO2, CO, CH4 and N2O. However, for all ecosystems except croplands, C uptake exceeds C release and us such 210 ± 70 Tg C y−1 from fossil fuel burning is removed from the atmosphere and sequestered in both terrestrial and inland aquatic ecosystems. If the C cost for ecosystem management is taken into account, the net uptake of ecosystems was estimated to decrease by 45% but still indicates substantial C-sequestration. Also, when the balance is extended from CO2 towards the main GHGs, C-uptake by terrestrial and aquatic ecosystems is compensated for by emissions of GHGs. As such the European ecosystems are unlikely to contribute to mitigating the effects of climate change.

Description: During 2008 and 2009 we applied coincident Earth observation data collected from multiple sensors (RA2, AATSR and MERIS, mounted on the European Space Agency satellite Envisat) to characterise environmental conditions and net sea-air fluxes of CO2 in three Arctic seas (Greenland, Barents, Kara) to assess net CO2 sink sensitivity due to changes in temperature, salinity and sea ice duration arising from future climate scenarios. During the study period the Greenland and Barents Seas were net sinks for atmospheric CO2, with sea-air fluxes of −34±13 and −13±6 Tg C yr−1, respectively and the Kara Sea was a weak net CO2 source with a sea-air flux of +1.5±1.1 Tg C yr−1. The combined net CO2 sea-air flux from all three was −45±18 Tg C yr−1. In a sensitivity analysis we varied temperature, salinity and sea ice duration. Variations in temperature and salinity led to modification of the transfer velocity, solubility and partial pressure of CO2 taking into account the resultant variations in alkalinity and dissolved organic carbon (DOC). Our results showed that warming had a strong positive effect on the annual net sea-air flux of CO2 (i.e. reducing the sink), freshening had a strong negative effect and reduced sea ice duration had a small but measurable positive effect. In the climate change scenario examined, the effects of warming in just over a decade of climate change up to 2020 outweighed the combined effects of freshening and reduced sea ice duration. Collectively these effects gave a net sea-air flux change of +3.5 Tg C in the Greenland Sea, +5.5 Tg C in the Barents Sea and +1.4 Tg C in the Kara Sea, reducing the Greenland and Barents sinks by 10% and 50% respectively, and increasing the weak Kara Sea source by 64%. Overall, the regional flux changed by +10.4 Tg C, reducing the regional sink by 23%. In terms of CO2 sink strength we conclude that the Barents Sea is the most susceptible of the three regions to the climate changes examined. Our results imply that the region will cease to be a net CO2 sink by 2060.

Description: During the MALINA cruise (summer 2009) an extensive effort was undertaken to isolate phytoplankton strains from the North East (NE) Pacific Ocean, the Bering Strait, and the Beaufort Sea. Strains were isolated by flow cytometry sorting (FCS) and pipetting before or after phytoplankton enrichment of seawater samples. Strains were isolated both onboard and back in the laboratory and cultured at 4 °C under light/dark conditions. Overall, we isolated and characterised by light microscopy and 18S rRNA gene sequencing 104 strains of photosynthetic flagellates which grouped into 21 genotypes (defined by 99.5% 18S rRNA gene sequence similarity) mainly affiliated to Chlorophyta and Heterokontophyta. The taxon most frequently isolated was an Arctic ecotype of the green algal genus Micromonas (Arctic Micromonas) which was almost the only phytoplankter recovered within picoplankton (≤ 2 μm) size range. Strains of Arctic Micromonas as well as three unidentified strains related to the same genus were identified in further details by sequencing the Internal Transcribed Spacer (ITS) region of the rRNA operon. The MALINA Micromonas strains share identical 18S rRNA and ITS sequences suggesting high genetic homogeneity within Arctic Micromonas. The unidentified strains form a genotype likely belonging to a new genus within the family Mamiellaceae to which Micromonas belongs. Other green algae genotypes from the genera Nephroselmis, Chlamydomonas, Pyramimonas were also isolated whereas Heterokontophyta included Pelagophyceae, Dictyochophyceae and Chrysophyceae. Dictyochophyceae included Pedinellales which could not be identified to the genus level whereas Chrysophyceae comprised Dinobryon faculiferum. Moreover, we isolated Rhodomonas sp. as well as a few Haptophyta and dinoflagellates. We identified the dinoflagellate Woloszynskia cincta by Scanning Electron Microscopy (SEM) and 28S rRNA gene sequencing. Our morphological analyses show that this species possess the diagnostic features of the genus Biecheleria, and the 28S rRNA gene topology corroborates this affiliation. We thus propose the transfer of W. cincta to the genus Biecheleria and its recombination as Biecheleria cincta.

Description: The relevance of biological Si cycling for dissolved silica (DSi) export from terrestrial biogeosystems is still in debate. Even in systems showing a high content of weatherable minerals, like Cambisols on volcanic tuff, biogenic Si (BSi) might contribute 〉 50% to total DSi (Gerard et~al., 2008). However, the actual number of biogeosystem studies is rather limited for generalised conclusions. To cover one end of controlling factors on DSi – weatherable minerals content – we studied a~forested site with absolute quartz dominance (〉 95%). Hence, we hypothesise minimal effects of chemical weathering of silicates on DSi. During a~four year observation period (May 2007–April 2011) we quantified (i) internal and external Si fluxes of a temperate-humid biogeosystem (beech, 120 yr) by BIOME-BGC (vers. ZALF), (ii) related Si budgets, and, (iii) Si pools in soil and beech, chemically as well as by SEM-EDX. For the first time both compartments of biogenic Si in soils were analysed, i.e. phytogenic and zoogenic Si pool (testate amoebae). We quantified an average Si plant uptake of 35 kg Si ha−1 yr−1 – most of which is recycled to the soil by litterfall – and calculated an annual biosilicification from idiosomic testate amoebae of 17 kg Si ha−1. High DSi concentrations (6 mg l−1) and DSi exports (12 kg Si ha−1 yr−1) could not be explained by chemical weathering of feldspars or quartz dissolution. Instead, dissolution of a relictic phytolith Si pool seems to be the main process for the DSi observed. We identified forest management, i.e. selective extraction of pine trees 20 yr ago followed by a disappearance of grasses, as the most probable control for the phenomena observed and hypothesised the biogeosystem to be in a transient state in terms of Si cycling.

Description: High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or light detection and ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high-resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (〉 40%) of the Colombian Amazon – a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon maps have 14% uncertainty at 1 ha resolution, and the regional map based on stratification has 28% uncertainty in any given hectare. High-resolution approaches with quantifiable pixel-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.