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Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990–2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g., anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4 yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH–HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1, respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4590875 (Petrescu et al., 2020b).

Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO2), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO2 for the European Union and UK (EU27 + UK), derived from a combination of state-of-the-art bottom-up (BU) and top-down (TD) data sources and models. Given the wide scope of the work and the variety of datasets involved, this study focuses on identifying essential questions which need to be answered to properly understand the differences between various datasets, in particular with regards to the less-well-characterized fluxes from managed ecosystems. The work integrates recent emission inventory data, process-based ecosystem model results, data-driven sector model results and inverse modeling estimates over the period 1990–2018. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported under the UNFCCC in 2019, aiming to assess and understand the differences between approaches. For the uncertainties in NGHGIs, we used the standard deviation obtained by varying parameters of inventory calculations, reported by the member states following the IPCC Guidelines. Variation in estimates produced with other methods, like atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arises from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. In comparing NGHGIs with other approaches, a key source of uncertainty is that related to different system boundaries and emission categories (CO2 fossil) and the use of different land use definitions for reporting emissions from land use, land use change and forestry (LULUCF) activities (CO2 land). At the EU27 + UK level, the NGHGI (2019) fossil CO2 emissions (including cement production) account for 2624 Tg CO2 in 2014 while all the other seven bottom-up sources are consistent with the NGHGIs and report a mean of 2588 (± 463 Tg CO2). The inversion reports 2700 Tg CO2 (± 480 Tg CO2), which is well in line with the national inventories. Over 2011–2015, the CO2 land sources and sinks from NGHGI estimates report −90 Tg C yr−1 ± 30 Tg C yr−1 while all other BU approaches report a mean sink of −98 Tg C yr−1 (± 362 Tg of C from dynamic global vegetation models only). For the TD model ensemble results, we observe a much larger spread for regional inversions (i.e., mean of 253 Tg C yr−1 ± 400 Tg C yr−1). This concludes that (a) current independent approaches are consistent with NGHGIs and (b) their uncertainty is too large to allow a verification because of model differences and probably also because of the definition of “CO2 flux” obtained from different approaches. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4626578 (Petrescu et al., 2020a).

Emission of greenhouse gases (GHGs) and removals from land, including both anthropogenic and natural fluxes, require reliable quantification, including estimates of uncertainties, to support credible mitigation action under the Paris Agreement. This study provides a state-of-the-art scientific overview of bottom-up anthropogenic emissions data from agriculture, forestry and other land use (AFOLU) in the European Union (EU281). The data integrate recent AFOLU emission inventories with ecosystem data and land carbon models and summarize GHG emissions and removals over the period 1990–2016. This compilation of bottom-up estimates of the AFOLU GHG emissions of European national greenhouse gas inventories (NGHGIs), with those of land carbon models and observation-based estimates of large-scale GHG fluxes, aims at improving the overall estimates of the GHG balance in Europe with respect to land GHG emissions and removals. Whenever available, we present uncertainties, its propagation and role in the comparison of different estimates. While NGHGI data for the EU28 provide consistent quantification of uncertainty following the established IPCC Guidelines, uncertainty in the estimates produced with other methods needs to account for both within model uncertainty and the spread from different model results. The largest inconsistencies between EU28 estimates are mainly due to different sources of data related to human activity, referred to here as activity data (AD) and methodologies (tiers) used for calculating emissions and removals from AFOLU sectors. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.3662371 (Petrescu et al., 2020).

Ice-ridge Yedoma terrain is susceptible to vertical surface displacements by thaw and refreeze of ground ice, and geomorphological processes of mass wasting, erosion and sedimentation. Here we explore the relation between a 3 year data set of InSAR measurements of vertical surface displacements during the thaw season, and geomorphological features in an area in the Indigirka Lowlands, Northeast Siberia. The geomorphology is presented in a geomorphological map, based on interpretation of high resolution visible spectrum satellite imagery, field surveys and available data from paleo-environmental research. The main landforms comprise overlapping drained thaw lake basins and lakes, erosion remnants of Late Pleistocene Yedoma deposits, and a floodplain of a high-sinuosity anastomosing river with ancient river terrace remnants. The spatial distribution of drained thaw lake basins and Yedoma erosion remnants in the study area and its surroundings is influenced by neotectonic movements. The 3 years of InSAR measurement include 2 years of high snowfall and extreme river flooding (2017–2018) and 1 year of modest snowfall, early spring and warm summer (2019). The magnitude of surface displacements varies among the years, and show considerable spatial variation. Distinct spatial clusters of displacement trajectories can be discerned, which relate to geomorphological processes and ground ice conditions. Strong subsidence occurred in particular in 2019. In the wet year of 2017, marked heave occurred at Yedoma plateau surfaces, likely by ice accumulation at the top of the permafrost driven by excess precipitation. The spatial variability of surface displacements is high. This is explored by statistical analysis, and is attributed to the interaction of various processes. Next to ground ice volume change, also sedimentation (peat, colluvial deposition) and shrinkage or swelling of soils with changing water content may have contributed. Tussock tundra areas covered by the extreme 2017 and 2018 spring floods show high subsidence rates and an increase of midsummer thaw depths. We hypothesize that increased flood heights along Siberian lowland rivers potentially induce deeper thaw and subsidence on floodplain margins, and also lowers the drainage thresholds of thaw lakes. Both mechanisms tend to increase floodplain area. This may increase CH4 emission from floodplains, but also may enhance carbon storage in floodplain sedimentary environments.

Natural wetlands constitute the largest and most uncertain source of methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process (“bottom-up”) or inversion (“top-down”) models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45∘ N). Eddy covariance data from 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash–Sutcliffe model efficiency =0.47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3–41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4–39.9) or 38 (25.9–49.5) Tg(CH4) yr−1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available at https://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019).

Inland waters are large contributors to global carbon dioxide (CO2) emissions, in part due to the vulnerability of dissolved organic matter (DOM) to microbial decomposition and respiration to CO2 during transport through aquatic systems. To assess the degree of this vulnerability, aquatic DOM is often incubated in standardized biolability assays. These assays isolate the dissolved fraction of aquatic OM by size filtration prior to incubation. We test whether this size selection has an impact on the bacterial community composition and the consequent dynamics of DOM degradation using three different filtration strategies: 0.2 µm (filtered and inoculated), 0.7 µm (generally the most common DOM filter size) and 106 µm (unfiltered). We found that bacterial community composition, based on 16S rRNA amplicon sequencing, was significantly affected by the different filter sizes. At the same time, the filtration strategy also affected the DOM degradation dynamics, including the δ13C signature. However, the dynamics of these two responses were decoupled, suggesting that filtration primarily influences biolability assays through bacterial abundance and the presence of their associated predators. By the end of the 41-day incubations all treatments tended to converge on a common total DOM biolability level, with the 0.7 µm filtered incubations reaching this point the quickest. These results suggest that assays used to assess the total biolability of aquatic DOM should last long enough to remove filtration artefacts in the microbial population. Filtration strategy should also be taken into account when comparing results across biolability assays.

The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project aims to advance the development of land evaporation estimates on global and regional scales. Its main objective is the derivation, validation, and intercomparison of a group of existing evaporation retrieval algorithms driven by a common forcing data set. Three commonly used process-based evaporation methodologies are evaluated: the Penman–Monteith algorithm behind the official Moderate Resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Global Land Evaporation Amsterdam Model (GLEAM), and the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL). The resulting global spatiotemporal variability of evaporation, the closure of regional water budgets, and the discrete estimation of land evaporation components or sources (i.e. transpiration, interception loss, and direct soil evaporation) are investigated using river discharge data, independent global evaporation data sets and results from previous studies. In a companion article (Part 1), Michel et al. (2016) inspect the performance of these three models at local scales using measurements from eddy-covariance towers and include in the assessment the Surface Energy Balance System (SEBS) model. In agreement with Part 1, our results indicate that the Priestley and Taylor products (PT-JPL and GLEAM) perform best overall for most ecosystems and climate regimes. While all three evaporation products adequately represent the expected average geographical patterns and seasonality, there is a tendency in PM-MOD to underestimate the flux in the tropics and subtropics. Overall, results from GLEAM and PT-JPL appear more realistic when compared to surface water balances from 837 globally distributed catchments and to separate evaporation estimates from ERA-Interim and the model tree ensemble (MTE). Nonetheless, all products show large dissimilarities during conditions of water stress and drought and deficiencies in the way evaporation is partitioned into its different components. This observed inter-product variability, even when common forcing is used, suggests that caution is necessary in applying a single data set for large-scale studies in isolation. A general finding that different models perform better under different conditions highlights the potential for considering biome- or climate-specific composites of models. Nevertheless, the generation of a multi-product ensemble, with weighting based on validation analyses and uncertainty assessments, is proposed as the best way forward in our long-term goal to develop a robust observational benchmark data set of continental evaporation.

Assigning proper prior uncertainties for inverse modelling of CO2 is of high importance, both to regularise the otherwise ill-constrained inverse problem and to quantitatively characterise the magnitude and structure of the error between prior and "true" flux. We use surface fluxes derived from three biosphere models – VPRM, ORCHIDEE, and 5PM – and compare them against daily averaged fluxes from 53 eddy covariance sites across Europe for the year 2007 and against repeated aircraft flux measurements encompassing spatial transects. In addition we create synthetic observations using modelled fluxes instead of the observed ones to explore the potential to infer prior uncertainties from model–model residuals. To ensure the realism of the synthetic data analysis, a random measurement noise was added to the modelled tower fluxes which were used as reference. The temporal autocorrelation time for tower model–data residuals was found to be around 30 days for both VPRM and ORCHIDEE but significantly different for the 5PM model with 70 days. This difference is caused by a few sites with large biases between the data and the 5PM model. The spatial correlation of the model–data residuals for all models was found to be very short, up to few tens of kilometres but with uncertainties up to 100 % of this estimation. Propagating this error structure to annual continental scale yields an uncertainty of 0.06 Gt C and strongly underestimates uncertainties typically used from atmospheric inversion systems, revealing another potential source of errors. Long spatial e-folding correlation lengths up to several hundreds of kilometres were determined when synthetic data were used. Results from repeated aircraft transects in south-western France are consistent with those obtained from the tower sites in terms of spatial autocorrelation (35 km on average) while temporal autocorrelation is markedly lower (13 days). Our findings suggest that the different prior models have a common temporal error structure. Separating the analysis of the statistics for the model data residuals by seasons did not result in any significant differences of the spatial e-folding correlation lengths.

The instrumental surface air temperature record has been used in several statistical studies to assess the relative role of natural and anthropogenic drivers of climate change. The results of those studies varied considerably, with anthropogenic temperature trends over the past 25–30 years suggested to range from 0.07 to 0.20 °C decade−1. In this short communication, we assess the origin of these differences and highlight the inverse relation between the temperature trend of the past 30 years and the weight given to the Atlantic Multidecadal Oscillation (AMO) as an explanatory factor in the multiple linear regression (MLR) tool that is usually employed. We highlight that robust MLR outcomes require a better understanding of the AMO in general and, more specifically, of its characterization. Our results indicate that both the high and the low end of the anthropogenic trend over the past 30 years found in previous studies are unlikely and that a transient climate response of 1.6 (1.0–3.3) °C best captures the historic instrumental temperature record.

Most plot-scale methane emission models – of which many have been developed in the recent past – are validated using data collected with the closed-chamber technique. This method, however, suffers from a low spatial representativeness and a poor temporal resolution. Also, during a chamber-flux measurement the air within a chamber is separated from the ambient atmosphere, which negates the influence of wind on emissions. Additionally, some methane models are validated by upscaling fluxes based on the area-weighted averages of modelled fluxes, and by comparing those to the eddy covariance (EC) flux. This technique is rather inaccurate, as the area of upscaling might be different from the EC tower footprint, therefore introducing significant mismatch. In this study, we present an approach to validate plot-scale methane models with EC observations using the footprint-weighted average method. Our results show that the fluxes obtained by the footprint-weighted average method are of the same magnitude as the EC flux. More importantly, the temporal dynamics of the EC flux on a daily timescale are also captured (r2 = 0.7). In contrast, using the area-weighted average method yielded a low (r2 = 0.14) correlation with the EC measurements. This shows that the footprint-weighted average method is preferable when validating methane emission models with EC fluxes for areas with a heterogeneous and irregular vegetation pattern.

In order to better address the feedbacks between climate and wetland methane (CH4) emissions, we tested several mechanistic improvements to the wetland CH4 emission model Peatland-VU with a longer Arctic data set than any other model: (1) inclusion of an improved hydrological module, (2) incorporation of a gross primary productivity (GPP) module, and (3) a more realistic soil-freezing scheme. A long time series of field measurements (2003–2010) from a tundra site in northeastern Siberia is used to validate the model, and the generalized likelihood uncertainty estimation (GLUE) methodology is used to test the sensitivity of model parameters. Peatland-VU is able to capture both the annual magnitude and seasonal variations of the CH4 flux, water table position, and soil thermal properties. However, detailed daily variations are difficult to evaluate due to data limitation. Improvements due to the inclusion of a GPP module are less than anticipated, although this component is likely to become more important at larger spatial scales because the module can accommodate the variations in vegetation traits better than at plot scale. Sensitivity experiments suggest that the methane production rate factor, the methane plant oxidation parameter, the reference temperature for temperature-dependent decomposition, and the methane plant transport rate factor are the most important parameters affecting the data fit, regardless of vegetation type. Both wet and dry vegetation cover are sensitive to the minimum water table level; the former is also sensitive to the runoff threshold and open water correction factor, and the latter to the subsurface water evaporation and evapotranspiration correction factors. These results shed light on model parameterization and future improvement of CH4 modelling. However, high spatial variability of CH4 emissions within similar vegetation/soil units and data quality prove to impose severe limits on model testing and improvement.

A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The paper is addressed to scientists, policymakers, and funding agencies who need to have a global picture of the current state of the (diverse) carbon observations. We identify the current state of carbon observations, and the needs and notional requirements for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion of the ground-based observation networks required to reach the high spatial resolution for CO2 and CH4 fluxes, and for carbon stocks for addressing policy-relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over areas such as the southern oceans, tropical forests, and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote-sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with ground-based data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO2 proxy measurements such as radiocarbon in CO2 and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.

Croplands cover about 12% of the ice-free terrestrial land surface. Compared with natural ecosystems, croplands have distinct characteristics due to anthropogenic influences. Their global gross primary production (GPP) is not well constrained and estimates vary between 8.2 and 14.2 Pg C yr−1. We quantified global cropland GPP using a light use efficiency (LUE) model, employing satellite observations and survey data of crop types and distribution. A novel step in our analysis was to assign a maximum light use efficiency estimate (ϵ*GPP) to each of the 26 different crop types, instead of taking a uniform value as done in the past. These ϵ*GPP values were calculated based on flux tower CO2 exchange measurements and a literature survey of field studies, and ranged from 1.20 to 2.96 g C MJ−1. Global cropland GPP was estimated to be 11.05 Pg C yr−1 in the year 2000. Maize contributed most to this (1.55 Pg C yr−1), and the continent of Asia contributed most with 38.9% of global cropland GPP. In the continental United States, annual cropland GPP (1.28 Pg C yr−1) was close to values reported previously (1.24 Pg C yr−1) constrained by harvest records, but our estimates of ϵ*GPP values were considerably higher. Our results are sensitive to satellite information and survey data on crop type and extent, but provide a consistent and data-driven approach to generate a look-up table of ϵ*GPP for the 26 crop types for potential use in other vegetation models.

Ammonia (NH3) emissions in the atmosphere have increased substantially over the past decades, largely because of intensive livestock production and use of fertilizers. As a short-lived species, NH3 is highly variable in the atmosphere and its concentration is generally small, except near local sources. While ground-based measurements are possible, they are challenging and sparse. Advanced infrared sounders in orbit have recently demonstrated their capability to measure NH3, offering a new tool to refine global and regional budgets. In this paper we describe an improved retrieval scheme of NH3 total columns from the measurements of the Infrared Atmospheric Sounding Interferometer (IASI). It exploits the hyperspectral character of this instrument by using an extended spectral range (800–1200 cm−1) where NH3 is optically active. This scheme consists of the calculation of a dimensionless spectral index from the IASI level1C radiances, which is subsequently converted to a total NH3 column using look-up tables built from forward radiative transfer model simulations. We show how to retrieve the NH3 total columns from IASI quasi-globally and twice daily above both land and sea without large computational resources and with an improved detection limit. The retrieval also includes error characterization of the retrieved columns. Five years of IASI measurements (1 November 2007 to 31 October 2012) have been processed to acquire the first global and multiple-year data set of NH3 total columns, which are evaluated and compared to similar products from other retrieval methods. Spatial distributions from the five years data set are provided and analyzed at global and regional scales. In particular, we show the ability of this method to identify smaller emission sources than those previously reported, as well as transport patterns over the ocean. The five-year time series is further examined in terms of seasonality and interannual variability (in particular as a function of fire activity) separately for the Northern and Southern Hemispheres.

We investigated the impact of drought on interannual variability of net primary productivity (NPP) from 1997 to 2009, using the standardized precipitation evapotranspiration index (SPEI) drought index and satellite-derived vegetation greenness converted to NPP. SPEI is positive for wet conditions and negative for dry conditions. We found that SPEI and NPP were coupled and showed in-phase behaviour on a global scale. We then used the Köppen climate classification to study the SPEI–NPP relations regionally and found that while NPP and SPEI were positively related (high SPEI, high NPP) in arid and in seasonal dry regions, the opposite occurs in most boreal regions (high SPEI, low NPP). High intensity drought events, such as the 2003 drought in Europe were picked up by our analysis. Our findings suggest that the strong positive relation between global average moisture availability and NPP consists of a composite of the positive relation across dry regions and the coherent NPP decline during and after intensive drought events in humid regions. Importantly, we also found that there are many areas on the globe that show no strong correlation between drought and NPP.

Land evapotranspiration (ET) estimates are available from several global data sets. Here, monthly global land ET synthesis products, merged from these individual data sets over the time periods 1989–1995 (7 yr) and 1989–2005 (17 yr), are presented. The merged synthesis products over the shorter period are based on a total of 40 distinct data sets while those over the longer period are based on a total of 14 data sets. In the individual data sets, ET is derived from satellite and/or in situ observations (diagnostic data sets) or calculated via land-surface models (LSMs) driven with observations-based forcing or output from atmospheric reanalyses. Statistics for four merged synthesis products are provided, one including all data sets and three including only data sets from one category each (diagnostic, LSMs, and reanalyses). The multi-annual variations of ET in the merged synthesis products display realistic responses. They are also consistent with previous findings of a global increase in ET between 1989 and 1997 (0.13 mm yr−2 in our merged product) followed by a significant decrease in this trend (−0.18 mm yr−2), although these trends are relatively small compared to the uncertainty of absolute ET values. The global mean ET from the merged synthesis products (based on all data sets) is 493 mm yr−1 (1.35 mm d−1) for both the 1989–1995 and 1989–2005 products, which is relatively low compared to previously published estimates. We estimate global runoff (precipitation minus ET) to 263 mm yr−1 (34 406 km3 yr−1) for a total land area of 130 922 000 km2. Precipitation, being an important driving factor and input to most simulated ET data sets, presents uncertainties between single data sets as large as those in the ET estimates. In order to reduce uncertainties in current ET products, improving the accuracy of the input variables, especially precipitation, as well as the parameterizations of ET, are crucial.

The role of different sources and sinks of CH4 in changes in atmospheric methane ([CH4]) concentration during the last 100 000 yr is still not fully understood. In particular, the magnitude of the change in wetland CH4 emissions at the Last Glacial Maximum (LGM) relative to the pre-industrial period (PI), as well as during abrupt climatic warming or Dansgaard–Oeschger (D–O) events of the last glacial period, is largely unconstrained. In the present study, we aim to understand the uncertainties related to the parameterization of the wetland CH4 emission models relevant to these time periods by using two wetland models of different complexity (SDGVM and ORCHIDEE). These models have been forced by identical climate fields from low-resolution coupled atmosphere–ocean general circulation model (FAMOUS) simulations of these time periods. Both emission models simulate a large decrease in emissions during LGM in comparison to PI consistent with ice core observations and previous modelling studies. The global reduction is much larger in ORCHIDEE than in SDGVM (respectively −67 and −46%), and whilst the differences can be partially explained by different model sensitivities to temperature, the major reason for spatial differences between the models is the inclusion of freezing of soil water in ORCHIDEE and the resultant impact on methanogenesis substrate availability in boreal regions. Besides, a sensitivity test performed with ORCHIDEE in which the methanogenesis substrate sensitivity to the precipitations is modified to be more realistic gives a LGM reduction of −36%. The range of the global LGM decrease is still prone to uncertainty, and here we underline its sensitivity to different process parameterizations. Over the course of an idealized D–O warming, the magnitude of the change in wetland CH4 emissions simulated by the two models at global scale is very similar at around 15 Tg yr−1, but this is only around 25% of the ice-core measured changes in [CH4]. The two models do show regional differences in emission sensitivity to climate with much larger magnitudes of northern and southern tropical anomalies in ORCHIDEE. However, the simulated northern and southern tropical anomalies partially compensate each other in both models limiting the net flux change. Future work may need to consider the inclusion of more detailed wetland processes (e.g. linked to permafrost or tropical floodplains), other non-wetland CH4 sources or different patterns of D–O climate change in order to be able to reconcile emission estimates with the ice-core data for rapid CH4 events.

We determine the net land to atmosphere flux of carbon in Russia, including Ukraine, Belarus and Kazakhstan, using inventory-based, eddy covariance, and inversion methods. Our high boundary estimate is −342 Tg C yr−1 from the eddy covariance method, and this is close to the upper bounds of the inventory-based Land Ecosystem Assessment and inverse models estimates. A lower boundary estimate is provided at −1350 Tg C yr−1 from the inversion models. The average of the three methods is −613.5 Tg C yr−1. The methane emission is estimated separately at 41.4 Tg C yr−1. These three methods agree well within their respective error bounds. There is thus good consistency between bottom-up and top-down methods. The forests of Russia primarily cause the net atmosphere to land flux (−692 Tg C yr−1 from the LEA. It remains however remarkable that the three methods provide such close estimates (−615, −662, −554 Tg C yr–1) for net biome production (NBP), given the inherent uncertainties in all of the approaches. The lack of recent forest inventories, the few eddy covariance sites and associated uncertainty with upscaling and undersampling of concentrations for the inversions are among the prime causes of the uncertainty. The dynamic global vegetation models (DGVMs) suggest a much lower uptake at −91 Tg C yr−1, and we argue that this is caused by a high estimate of heterotrophic respiration compared to other methods.

Soil moisture availability is important in regulating photosynthesis and controlling land surface-climate feedbacks at both the local and global scale. Recently, global remote-sensing datasets for soil moisture have become available. In this paper we assess the possibility of using remotely sensed soil moisture – AMSR-E (LPRM) – to similate soil moisture dynamics of the process-based vegetation model ORCHIDEE by evaluating the correspondence between these two products using both correlation and autocorrelation analyses. We find that the soil moisture product of AMSR-E (LPRM) and the simulated soil moisture in ORCHIDEE correlate well in space and time, in particular when considering the root zone soil moisture of ORCHIDEE. However, the root zone soil moisture in ORCHIDEE has on average a higher temporal autocorrelation relative to AMSR-E (LPRM) and in situ measurements. This may be due to the different vertical depth of the two products – AMSR-E (LPRM) at the 2–5 cm surface depth and ORCHIDEE at the root zone (max. 2 m) depth – to uncertainty in precipitation forcing in ORCHIDEE, and to the fact that the structure of ORCHIDEE consists of a single-layer deep soil, which does not allow simulation of the proper cascade of time scales that characterize soil drying after each rain event. We conclude that assimilating soil moisture, using AMSR-E (LPRM) in a land surface model like ORCHIDEE with an improved hydrological model of more than one soil layer, may significantly improve the soil moisture dynamics, which could lead to improved CO2 and energy flux predictions.

We have implemented six different inverse carbon flux estimation methods in a regional carbon dioxide (CO2) flux modeling system for the Netherlands. The system consists of the Regional Atmospheric Mesoscale Modeling System (RAMS) coupled to a simple carbon flux scheme which is run in a coupled fashion on relatively high resolution (10 km). Using an Ensemble Kalman filter approach we try to estimate spatiotemporal carbon exchange patterns from atmospheric CO2 mole fractions over the Netherlands for a two week period in spring 2008. The focus of this work is the different strategies that can be employed to turn first-guess fluxes into optimal ones, which is known as a fundamental design choice that can affect the outcome of an inversion significantly. Different state-of-the-art approaches with respect to the estimation of net ecosystem exchange (NEE) are compared quantitatively: (1) where NEE is scaled by one linear multiplication factor per land-use type, (2) where the same is done for photosynthesis (GPP) and respiration (R) separately with varying assumptions for the correlation structure, (3) where we solve for those same multiplication factors but now for each grid box, and (4) where we optimize physical parameters of the underlying biosphere model for each land-use type. The pattern to be retrieved in this pseudo-data experiment is different in nearly all aspects from the first-guess fluxes, including the structure of the underlying flux model, reflecting the difference between the modeled fluxes and the fluxes in the real world. This makes our study a stringent test of the performance of these methods, which are currently widely used in carbon cycle inverse studies. Our results show that all methods struggle to retrieve the spatiotemporal NEE distribution, and none of them succeeds in finding accurate domain averaged NEE with correct spatial and temporal behavior. The main cause is the difference between the structures of the first-guess and true CO2 flux models used. Most methods display overconfidence in their estimate as a result. A commonly used daytime-only sampling scheme in the transport model leads to compensating biases in separate GPP and R scaling factors that are readily visible in the nighttime mixing ratio predictions of these systems. Overall, we recommend that the estimate of NEE scaling factors should not be used in this regional setup, while estimating bias factors for GPP and R for every grid box works relatively well. The biosphere parameter inversion performs good compared to the other inversions at simultaneously producing space and time patterns of fluxes and CO2 mixing ratios, but non-linearity may significantly reduce the information content in the inversion if true parameter values are far from the prior estimate. Our results suggest that a carefully designed biosphere model parameter inversion or a pixel inversion of the respiration and GPP multiplication factors are from the tested inversions the most promising tools to optimize spatiotemporal patterns of NEE.

The role of the microbial processes governing methane emissions from tundra ecosystems is receiving increasing attention. Recently, cooperation between methanotrophic bacteria and submerged Sphagnum was shown to reduce methane emissions but also to supply CO2 for photosynthesis for the plant. Although this process was shown to be important in the laboratory, the differences that exist in methane emissions from inundated vegetation types with or without Sphagnum in the field have not been linked to these bacteria before. In this study, chamber flux measurements, an incubation study and a process model were used to investigate the drivers and controls on the relative difference in methane emissions between a submerged Sphagnum/sedge vegetation type and an inundated sedge vegetation type without Sphagnum. It was found that methane emissions in the Sphagnum-dominated vegetation type were 50 % lower than in the vegetation type without Sphagnum. A model sensitivity analysis showed that these differences could not sufficiently be explained by differences in methane production and plant transport. The model, combined with an incubation study, indicated that methane oxidation by endophytic bacteria, living in cooperation with submerged Sphagnum, plays a significant role in methane cycling at this site. This result is important for spatial upscaling as oxidation by these bacteria is likely involved in 15 % of the net methane emissions at this tundra site. Our findings support the notion that methane-oxidizing bacteria are an important factor in understanding the processes behind methane emissions in tundra.

A process-based methodology is applied to estimate land-surface evaporation from multi-satellite information. GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) combines a wide range of remotely-sensed observations to derive daily actual evaporation and its different components. Soil water stress conditions are defined from a root-zone profile of soil moisture and used to estimate transpiration based on a Priestley and Taylor equation. The methodology also derives evaporationfrom bare soil and snow sublimation. Tall vegetation rainfall interception is independently estimated by means of the Gash analytical model. Here, GLEAM is applied daily, at global scale and a quarter degree resolution. Triple collocation is used to calculate the error structure of the evaporation estimates and test the relative merits of two different precipitation inputs. The spatial distribution of evaporation – and its different components – is analysed to understand the relative importance of each component over different ecosystems. Annual land evaporation is estimated as 67.9 × 103 km3, 80% corresponding to transpiration, 11% to interception loss, 7% to bare soil evaporation and 2% snow sublimation. Results show that rainfall interception plays an important role in the partition of precipitation into evaporation and water available for runoff at a continental scale. This study gives insights into the relative importance of precipitation and net radiation in driving evaporation, and how the seasonal influence of these controls varies over different regions. Precipitation is recognised as an important factor driving evaporation, not only in areas that have limited soil water availability, but also in areas of high rainfall interception and low available energy.

No abstract available.

This paper outlines a new strategy to derive evaporation from satellite observations. The approach uses a variety of satellite-sensor products to estimate daily evaporation at a global scale and 0.25 degree spatial resolution. Central to this methodology is the use of the Priestley and Taylor (PT) evaporation model. The minimalistic PT equation combines a small number of inputs, the majority of which can be detected from space. This reduces the number of variables that need to be modelled. Key distinguishing features of the approach are the use of microwave-derived soil moisture, land surface temperature and vegetation density, as well as the detailed estimation of rainfall interception loss. The modelled evaporation is validated against one year of eddy covariance measurements from 43 stations. The estimated annual totals correlate well with the stations' annual cumulative evaporation (R=0.80, N=43) and present a low average bias (−5%). The validation of the daily time series at each individual station shows good model performance in all vegetation types and climate conditions with an average correlation coefficient of R=0.83, still lower than the R=0.90 found in the validation of the monthly time series. The first global map of annual evaporation developed through this methodology is also presented.

Recent summer heat waves in Europe were found to be preceded by precipitation deficits in winter. Numerical studies suggest that these phenomena are dynamically linked by land-atmosphere interactions. However, there exists as yet no complete observational evidence that connects summer climate variability to winter precipitation and the relevant circulation patterns. In this paper, we investigate the functional responses of summer mean and maximum temperature (June–August, Tmean and Tmax) as well as soil moisture proxied by the self-calibrating Palmer drought severity index (scPDSI) to preceding winter precipitation (January–March, PJFM) for the period 1901–2005. All the analyzed summer fields show distinctive responses to PJFM over the Mediterranean. We estimate that 10 ~ 15% of the interannual variability of Tmax and Tmean over the Mediterranean is statistically forced by PJFM. For the scPDSI this amounts to 10 ~ 25%. Further analysis shows that these responses are highly correlated to the North Atlantic Oscillation (NAO) regime over the Mediterranean. We suggest that NAO modulates European summer temperature by controlling winter precipitation that initializes the moisture states that subsequently interact with temperature. This picture of relations between European summer climate and NAO as well as winter precipitation suggests potential for improved seasonal prediction of summer climate for particular extreme events.

Estimating carbon exchange at regional scales is paramount to understanding feedbacks between climate and the carbon cycle, but also to verifying climate change mitigation such as emission reductions and strategies compensating for emissions such as carbon sequestration. This paper discusses evidence for a number of important shortcomings of current generation modelling frameworks designed to provide regional scale budgets from atmospheric observations. Current top-down and bottom-up approaches targeted at deriving consistent regional scale carbon exchange estimates for biospheric and anthropogenic sources and sinks are hampered by a number of issues: we show that top-down constraints using point measurements made from tall towers, although sensitive to larger spatial scales, are however influenced by local areas much more strongly than previously thought. On the other hand, classical bottom-up approaches using process information collected at the local scale, such as from eddy covariance data, need up-scaling and validation on larger scales. We therefore argue for a combination of both approaches, implicitly providing the important local scale information for the top-down constraint, and providing the atmospheric constraint for up-scaling of flux measurements. Combining these data streams necessitates quantifying their respective representation errors, which are discussed. The impact of these findings on future network design is highlighted, and some recommendations are given.

We simulated meteorology and atmospheric CO2 transport over the Netherlands with the mesoscale model RAMS-Leaf3 coupled to the biospheric CO2 flux model 5PM. The results were compared with meteorological and CO2 observations, with emphasis on the tall tower of Cabauw. An analysis of the coupled exchange of energy, moisture and CO2 showed that the surface fluxes in the domain strongly influenced the atmospheric properties. The majority of the variability in the afternoon CO2 mixing ratio in the middle of the domain was determined by biospheric and fossil fuel CO2 fluxes in the limited area domain (640×640 km). Variation of the surface CO2 fluxes, reflecting the uncertainty of the parameters in the CO2 flux model 5PM, resulted in a range of simulated atmospheric CO2 mixing ratios of on average 11.7 ppm in the well-mixed boundary layer. Additionally, we found that observed surface energy fluxes and observed atmospheric temperature and moisture could not be reconciled with the simulations. Including this as an uncertainty in the simulation of surface energy fluxes changed simulated atmospheric vertical mixing and horizontal advection, leading to differences in simulated CO2 of on average 1.7 ppm. This is an important source of uncertainty and should be accounted for to avoid biased calculations of the CO2 mixing ratio, but it does not overwhelm the signal in the CO2 mixing ratio due to the uncertainty range of the surface CO2 fluxes.

Evaporation is a key parameter in the regional atmospheric water cycle. Precipitation recycling is defined as the contribution of water that evaporates from a region to precipitation within the same region. We apply a dynamic precipitation recycling model, which includes a dynamic moisture storage term, to calculate the warm season variability of the precipitation recycling over central Europe at a daily time scale for 2003 (dry) and 2006 (wet). For the central part of Europe advection is the most important contributor to precipitation. In dry spells in both years 2003 and 2006, when moisture of advective origin diminishes, local evaporation becomes an important contributor to precipitation (negative feedback). In two dry periods (June 2003 and July 2006) where there is enough moisture storage in the soil to continue evaporation, precipitation recycling is enhanced. In case studies we follow the path of an air column for days with high precipitation recycling to discuss the role of moisture recycling in land-atmosphere interactions. For 2 days with enough moisture availability (28 May 2003 and 5 July 2006) moisture particles stay long in the study area due to weak winds. By following the paths we show that the air is transported over land for a very long distance before it precipitates. It thus takes a considerable amount of time to traverse the region and capture moisture of evaporative origin. However, we hypothesize that the precipitation falling on those days still originates (partly) from oceanic sources, but that the triggering of precipitation may itself be a result of enhanced instability induced by soils, which still have sufficient moisture storage. In dry periods with enough moisture available precipitation recycling acts as a mechanism to keep the precipitation at a stable level. In August 2003 evaporation is affecting the precipitation recycling due to the lack of water availability caused by the dryness of the preceding spring and summer season. According to a Granger Causality test the evaporation in 2003 exerts the strongest causal impact on the precipitation recycling ratio. For the case study of 10 August 2003, the atmosphere is too dry to generate precipitation with exception of the mountainous regions due to orographical lifting.

Given the importance of Amazon rainforest in the global carbon and hydrological cycles, there is a need to parameterize and validate ecosystem gas exchange and vegetation models for this region in order to adequately simulate present and future carbon and water balances. In this study, a sun and shade canopy gas exchange model is calibrated and evaluated at five rainforest sites using eddy correlation measurements of carbon and energy fluxes. Results from the model-data evaluation suggest that with adequate parameterisation, photosynthesis models taking into account the separation of diffuse and direct irradiance and the dynamics of sunlit and shaded leaves can accurately represent photosynthesis in these forests. Also, stomatal conductance formulations that only take into account atmospheric demand fail to correctly simulate moisture and CO2 fluxes in forests with a pronounced dry season, particularly during afternoon conditions. Nevertheless, it is also the case that large uncertainties are associated not only with the eddy correlation data, but also with the estimates of ecosystem respiration required for model validation. To accurately simulate Gross Primary Productivity (GPP) and energy partitioning the most critical parameters and model processes are the quantum yield of photosynthetic uptake, the maximum carboxylation capacity of Rubisco, and simulation of stomatal conductance. Using this model-data synergy, we developed scaling functions to provide estimates of canopy photosynthetic parameters for a range of diverse forests across the Amazon region, utilising the best fitted parameter for maximum carboxylation capacity of Rubisco, and foliar nutrients (N and P) for all sites.

In their paper "Biotic pump of atmospheric moisture as driver of the hydrological cycle on land", Makarieva and Gorshkov (Hydrol. Earth Syst. Sci., 11, 1013–1033, 2007) derive from "previously unstudied" properties of atmospheric water vapor, the existence of a hitherto unknown "evaporative force". From this, a novel physical principle is deduced, according to which low-level air flows from regions with weak, to regions with strong evaporation. As such, natural forests are claimed to "suck in" moist air from the ocean, a process labeled the "biotic pump of atmospheric moisture". This commentary focuses on the physical foundations of the Biotic Pump Theory (BPT). It is shown that the "evaporative force" on which the theory is built, is not supported by basic physical principles. The problem of moisture transport and its dependence on vegetation cover considered by Makarieva and Gorshkov is certainly important, but cannot be solved along the lines proposed by them.

The current paper reviews the experimental setup of the CarboEurope Experimental Strategy (CERES) campaigns with the aim of providing an overview of the instrumentation used, the data-set and associated modelling. It then assesses progress in the field of regional observation and modelling of carbon fluxes, bringing the papers of this special issue into a somewhat broader context of analysis. Instrumental progress has been obtained in the field of remotely monitoring from tall towers and the experimental planning. Flux measurements from aircraft are now capable, within some constraints, to provide regular regional observations of fluxes of CO2, latent and sensible heat. Considerable effort still needs to be put into calibrating the surface schemes of models, as they have direct impact on the input of energy, moisture and carbon fluxes in the boundary layer. Overall, the mesoscale models appear to be capable of simulating the large scale dynamics of the region, but in the fine detail, like the precise horizontal and vertical CO2 field differences between the models still exist. These errors translate directly into transport uncertainty, when the forward simulations are used in inverse mode. Quantification of this uncertainty, including that of inadequate boundary layer height modelling, still remains a major challenge for state of the art mesoscale models. Progress in inverse models has been slow, but has shown that it is possible to estimate some of the errors involved, and that using the combination of observations. Overall, the capability to produce regional, high-resolution estimates of carbon exchange, exist in potential, but the routine application will require considerable effort, both in the experimental as in the modelling domain.

This paper describes a numerical interpretation of the April 2007, CarboEurope Regional Experiment Strategy (CERES) campaign, devoted to the study of the CO2 cycle at the regional scale. Four consecutive clear sky days with intensive observations of CO2 concentration, fluxes at the surface and in the boundary layer have been simulated with the Meso-NH mesoscale model, coupled to ISBA-A-gs land surface model. The main result of this paper is to show how aircraft observations of CO2 concentration have been used to identify surface model errors and to calibrate the CO2 driving component of the surface model. In fact, the comparisons between modelled and observed CO2 concentrations within the Atmospheric Boundary Layer (ABL) allow to calibrate and correct not only the parameterization of respired CO2 fluxes by the ecosystem but also the Leaf Area Index (LAI) of the dominating land cover. After this calibration, the paper describes systematic comparisons of the model outputs with numerous data collected during the CERES campaign, in April 2007. For instance, the originality of this paper is the spatial integration of the comparisons. In fact, the aircraft observations of CO2 concentration and fluxes and energy fluxes are used for the model validation from the local to the regional scale. As a conclusion, the CO2 budgeting approach from the mesoscale model shows that the winter croplands are assimilating more CO2 than the pine forest, at this stage of the year and this case study.

Inverse modelling of carbon sources and sinks requires an accurate quality estimate of the modelling framework to obtain a realistic estimate of the inferred fluxes and their uncertainties. So-called "representation errors" result from our inability to correctly represent point observations with simulated average values of model grid cells. They may add substantial uncertainty to the interpretation of atmospheric CO2 mixing ratio data. We simulated detailed variations in the CO2 mixing ratios with a high resolution (2 km) mesoscale model (RAMS) to estimate the representation errors introduced at larger model grid sizes of 10–100 km. We found that meteorology is the main driver of representation errors in our study causing spatial and temporal variations in the error estimate. Within the nocturnal boundary layer, the representation errors are relatively large and mainly caused by unresolved topography at lower model resolutions. During the day, convective structures, mesoscale circulations, and surface CO2 flux variability were found to be the main sources of representation errors. Interpreting observations near a mesoscale circulation as representative for air with the correct footprint relative to the front can reduce the representation error substantially. The remaining representation error is 0.5–1.5 ppm at 20–100 km resolution.

The carbon isotope of a leaf (δ13Cleaf) is generally more negative in riparian zones than in areas with low soil moisture content or rainfall input. In Central Amazonia, the small-scale topography is composed of plateaus and valleys, with plateaus generally having a lower soil moisture status than the valley edges in the dry season. Yet in the dry season, the nocturnal accumulation of CO2 is higher in the valleys than on the plateaus. Samples of sunlit leaves and atmospheric air were collected along a topographical gradient in the dry season to test whether the δ13Cleaf of sunlit leaves and the carbon isotope ratio of ecosystem respired CO2 (δ13CReco) may be more negative in the valley than those on the plateau. The δ13Cleaf was significantly more negative in the valley than on the plateau. Factors considered to be driving the observed variability in δ13Cleaf were: leaf nitrogen concentration, leaf mass per unit area (LMA), soil moisture availability, more negative carbon isotope ratio of atmospheric CO2 (δ13Ca) in the valleys during daytime hours, and leaf discrimination (Δleaf). The observed pattern of δ13Cleaf might suggest that water-use efficiency (WUE) is higher on the plateaus than in the valleys. However, there was no full supporting evidence for this because it remains unclear how much of the difference in δ13Cleaf was driven by physiology or &delta13Ca. The δ13CReco was more negative in the valleys than on the plateaus on some nights, whereas in others it was not. It is likely that lateral drainage of CO2 enriched in 13C from upslope areas might have happened when the nights were less stable. Biotic factors such as soil CO2 efflux (Rsoil) and the responses of plants to environmental variables such as vapor pressure deficit (D) may also play a role. The preferential pooling of CO2 in the low-lying areas of this landscape may confound the interpretation of δ13Cleaf and δ13CReco.

A Fast Methane Analyzer (FMA) is assessed for its applicability in a closed path eddy covariance field set-up in a peat meadow. The FMA uses off-axis integrated cavity output spectroscopy combined with a highly specific narrow band laser for the detection of CH4 and strongly reflective mirrors to obtain a laser path length of 2–20×103 m. Statistical testing and a calibration experiment showed high precision (7.8×10−3 ppb) and accuracy ( Due to erroneous measurements, spikes and periods of low turbulence the data series consisted for 26% of gaps. Observed CH4 fluxes consisted mainly of emission, showed a diurnal cycle, but were rather variable over. The average CH4 emission was 29.7 nmol m−2 s−1, while the typical maximum CH4 emission was approximately 80.0 nmol m−2 s−1 and the typical minimum flux was approximately 0.0 nmol m−2 s−1. The correspondence of the measurements with flux chamber measurements in the footprint was good and the observed CH4 emission rates were comparable with eddy covariance CH4 measurements in other peat areas. Additionally, three measurement techniques with lower sampling frequencies were simulated, which might give the possibility to measure CH4 fluxes without an external pump and save energy. Disjunct eddy covariance appeared to be the most reliable substitute for 10 Hz eddy covariance, while relaxed eddy accumulation gave reliable estimates of the fluxes over periods in the order of days or weeks.

Um estudo de modelagem utilizando o modelo unidimensional OSU-CAPS foi realizado com o intuito de investigar o desenvolvimento da Camada Limite Noturna (CLN), durante a época úmida na região Amazônica, visando aumentar os conhecimentos sobre seu funcionamento sob diferentes condições ambientais. O Modelo simulou a profundidade (h i) e a estrutura da CLN sobre 3 sítios experimentais (RM, Floresta e FNS) em Rondônia- RO, oeste da região Amazônica e estas simulações foram comparadas com observações locais . Verificou-se que o modelo consegue representar a estrutura e o desenvolvimento da CLN, sobre os 3 sítios As simulações mostraram que RM (transição floresta-pastagem) e a Floresta apresentaram características similares de desenvolvimento da CLN, principalmente no período entre 19 hl e 06hl. Para a FNS (área totalmente desmatada) as análises indicaram que a CLN tem um desenvolvimento parecido com aquele observado sobre áreas urbanas - onde uma camada de mistura turbulenta rasa é observada próximo à superfície, nas primeiras horas da noite.A modelling study using the OSU-CAPS model has been carried out in order to investigate the Nocturnal Boundary Layer (NBL) evolution during the wet season over the Amazon region to increase the existing knowledge about its dynamics under different environmental conditions. The model simulated the NBL depth (h i) and structure over 3 experimantal sites (RM, Forest and FNS) in Rondônia- RO, western Amazonian region, and these simulations have been compared to the observational. It was verified that the model has reasonably represented the NBL structure and evolution to the 3 sites. Simulations showed that RM (transiction forest-pasture) and the Forest sites have presented similar NBL development characteristics mainly for the period between 7 pm and 06 am. In the case of FNS (pasture) the analyses pointed out the NBL development to be similar to the one observed in urban areas - where a shalow mixing layer migth be watched close to the surface, in the evening.

Atmospheric CO2 modeling in interaction with the surface fluxes, at the regional scale is developed within the frame of the European project CarboEurope-IP and its Regional Experiment component. In this context, five meso-scale meteorological models at 2 km resolution participate in an intercomparison exercise. Using a common experimental protocol that imposes a large number of rules, two days of the CarboEurope Regional Experiment Strategy (CERES) campaign are simulated. A systematic evaluation of the models is done in confrontation with the observations, using statistical tools and direct comparisons. Thus, temperature and relative humidity at 2 m, wind direction, surface energy and CO2 fluxes, vertical profiles of potential temperature as well as in-situ CO2 concentrations comparisons between observations and simulations are examined. These comparisons reveal a cold bias in the simulated temperature at 2 m, the latent heat flux is often underestimated. Nevertheless, the CO2 concentrations heterogeneities are well captured by most of the models. This intercomparison exercise shows also the models ability to represent the meteorology and carbon cycling at the synoptic and regional scale in the boundary layer, but also points out some of the major shortcomings of the models.

Carbon dioxide and methane fluxes were measured at a tundra site near Chokurdakh, in the lowlands of the Indigirka river in north-east Siberia. This site is one of the few stations on Russian tundra and it is different from most other tundra flux stations in its continentality. A suite of methods was applied to determine the fluxes of NEE, GPP, Reco and methane, including eddy covariance, chambers and leaf cuvettes. Net carbon dioxide fluxes were high compared with other tundra sites, with NEE=−92 g C m−2 yr−1, which is composed of an Reco=+141 g C m−2 yr−1 and GPP=−232 g C m−2 yr−1. This large carbon dioxide sink may be explained by the continental climate, that is reflected in low winter soil temperatures (−14°C), reducing the respiration rates, and short, relatively warm summers, stimulating high photosynthesis rates. Interannual variability in GPP was dominated by the frequency of light limitation (Rg−2), whereas Reco depends most directly on soil temperature and time in the growing season, which serves as a proxy of the combined effects of active layer depth, leaf area index, soil moisture and substrate availability. The methane flux, in units of global warming potential, was +28 g C-CO2e m−2 yr−1, so that the greenhouse gas balance was −64 g C-CO2e m−2 yr−1. Methane fluxes depended only slightly on soil temperature and were highly sensitive to hydrological conditions and vegetation composition.

Globally, peat lands are considered to be a sink of CO2, but a source when drained. Additionally, wet peat lands are thought to emit considerable amounts of CH4 and N2O. Hitherto, reliable and integrated estimates of emissions and emission factors for this type of land cover have been lacking and the effects of wetland restoration on methane emissions have been poorly quantified. In this paper we estimate the full greenhouse gas (GHG) balance of a restored natural peat land by determining the fluxes of CO2, CH4 and N2O through atmosphere and water, while accounting for the different Global Warming Potentials (GWP's). The site is an abandoned agricultural peat meadow, which has been converted into a wetland nature reserve ten years ago, after which the water level was raised. GHG fluxes were measured continuously with an eddy covariance system (CO2) and flux chamber measurements (CH4 and N2O). Meteorological and hydrological measurements were collected as well. With growing seasons of respectively 192, 168 and 129 days, the annual net ecosystem exchange of CO2 (NEE) was −446+±83 g C m−2 yr−1 for 2004, −311±58 g C m−2 yr−1 for 2005 and −232±57 g m−2 yr−1 for 2006. Ecosystem respiration (Reco) was estimated as 869±668 g C m−2 yr−1 for 2004, 866±666 g C m−2 yr−1 for 2005 and 924±711 g C m−2 yr−1 for 2006. CH4 emissions from the saturated land and water surfaces were high compared to the relatively dry land. Annual weighted CH4 emissions were 31.27±20.40 g C m−2 yr−1 for 2005 and 32.27±21.08 g C m−2 yr−1 for 2006. N2O fluxes were too low to be of significance. The water balance of the area was dominated by precipitation and evapotranspiration and therefore fluxes of carbon and CH4 through seepage, infiltration and drainage were relatively small (17.25 g C m−2 yr−1). The carbon-balance consisted for the largest part of CO2 uptake, CO2 respiration and CH4 emission from water saturated land and water. CO2 emission has decreased significantly as result of the raised water table, while CH4 fluxes have increased. In GWP's the area was a small net GHG sink given as CO2-equiv. of −86 g m−2 yr−1 (over a 100-year period).

The aim of this study is to explain topography induced spatial variations in the diurnal cycles of assimilation and latent heat of Mediterranean forest. Spatial variations of the fluxes are caused by variations in weather conditions and in vegetation characteristics. Weather conditions reflect short-term effects of climate, whereas vegetation characteristics, through adaptation and acclimation, long-term effects of climate. In this study measurements of plant physiology and weather conditions are used to explain observed differences in the fluxes. A model is used to study which part of the differences in the fluxes is caused by weather conditions and which part by vegetation characteristics. Data were collected at four experimental sub-Mediterranean deciduous forest plots in a heterogeneous terrain with contrasting aspect, soil water availability, humidity and temperature. We used a sun-shade model to scale fluxes from leaf to canopy, and calculated the canopy energy balance. Parameter values were derived from measurements of light interception, leaf chamber photosynthesis, leaf nitrogen content and 13C isotope discrimination in leaf material. Leaf nitrogen content is a measure of photosynthetic capacity, and 13C isotope discrimination of water use efficiency. For validation, sap-flux based measurements of transpiration were used. The model predicted diurnal cycles of transpiration and stomatal conductance, both their magnitudes and differences in afternoon stomatal closure between slopes of different aspect within the confidence interval of the validation data. Weather conditions mainly responsible for the shape of the diurnal cycles, and vegetation parameters for the magnitude of the fluxes. Although the data do not allow for a quantification of the two effects, the differences in vegetation parameters and weather among the plots and the sensitivity of the fluxes to them suggest that the diurnal cycles were more strongly affected by spatial variations in vegetation parameters than by meteorological variables. This indicates that topography induced variations in vegetation parameters are of equal importance to the fluxes as topography induced variations in radiation, humidity and temperature.

Variations in climate, land-use and water consumption can have profound effects on river runoff. There is an increasing demand to study these factors at the regional to river basin-scale since these effects will particularly affect water resources management at this level. This paper presents a method that can help to differentiate between the effects of man-made hydrological developments and climate variability (including both natural variability and anthropogenic climate change) at the basin scale. We show and explain the relation between climate, water consumption and changes in runoff for the Krishna river basin in central India. River runoff variability due to observed climate variability and increased water consumption for irrigation and hydropower is simulated for the last 100 years (1901–2000) using the STREAM water balance model. Annual runoff under climate variability is shown to vary only by about 14–34 millimetres (6–15%). It appears that reservoir construction after 1960 and increasing water consumption has caused a persistent decrease in annual river runoff of up to approximately 123 mm (61%). Variation in runoff under climate variability only would have decreased over the period under study, but we estimate that increasing water consumption has caused runoff variability that is three times higher.

We summed estimates of the carbon balance of forests, grasslands, arable lands and peatlands to obtain country-specific estimates of the terrestrial carbon balance during the 1990s. Forests and grasslands were a net sink for carbon, whereas croplands were carbon sources in all European countries. Hence, countries dominated by arable lands tended to be losing carbon from their terrestrial ecosystems, whereas forest-dominated countries tended to be sequestering carbon. In some countries, draining and extraction of peatlands caused substantial reductions in the net carbon balance. Net terrestrial carbon balances were typically an order of magnitude smaller than the fossil fuel-related carbon emissions. Exceptions to this overall picture were countries where population density and industrialization are small. It is, however, of utmost importance to acknowledge that the typically small net carbon balance represents the small difference between two large but opposing fluxes: uptake by forests and grasslands and losses from arable lands and peatlands. This suggests that relatively small changes in either or both of these large component fluxes could induce large effects on the net total, indicating that mitigation schemes should not be discarded a priori. In the absence of carbon-oriented land management, the current net carbon uptake is bound to decline soon. Protecting it will require actions at three levels; a) maintaining the current sink activity of forests, b) altered agricultural management practices to reduce the emissions from arable soils or turn into carbon sinks and c) protecting current large reservoirs (wetlands and old forests), since carbon is lost more rapidly than sequestered.

Observations of the net ecosystem exchange of water and CO2 were made during two seasons in 2000 and 2001 above a Larch forest in Far East Siberia (Yakutsk). The measurements were obtained by eddy correlation. There is a very sharply pronounced growing season of 100 days when the forest is leaved. Maximum half hourly uptake rates are 18 µmol m-2 s-1; maximum respiration rates are 5 µmol m-2 s-1. Net annual sequestration of carbon was estimated at 160 gCm-2 in 2001. Applying no correction for low friction velocities added 60 g C m-2. The net carbon exchange of the forest was extremely sensitive to small changes in weather that may switch the forest easily from a sink to a source, even in summer. June was the month with highest uptake in 2001. The average evaporation rate of the forest approached 1.46 mm day-1 during the growing season, with peak values of 3 mm day-1 with an estimated annual evaporation of 213 mm, closely approaching the average annual rainfall amount. 2001 was a drier year than 2000 and this is reflected in lower evaporation rates in 2001 than in 2000. The surface conductance of the forest shows a marked response to increasing atmospheric humidity deficits. This affects the CO2 uptake and evaporation in a different manner, with the CO2 uptake being more affected. There appears to be no change in the relation between surface conductance and net ecosystem uptake normalized by the atmospheric humidity deficit at the monthly time scale. The response to atmospheric humidity deficit is an efficient mechanism to prevent severe water loss during the short intense growing season. The associated cost to the sequestration of carbon may be another explanation for the slow growth of these forests in this environment.

The objective of the present study is to test the performance of the ECMWF land surface module (LSM) developed by Viterbo and Beljaars (1995) and to identify primary future adjustments, focusing on the hydrological components. This was achieved by comparing off-line simulations against observations and a detailed state-of-the-art model over a range of experimental conditions. Results showed that the standard LSM, which uses fixed vegetation and soil parameter values, systematically underestimated evapotranspiration, partly due to underestimating bare soil evaporation, which appeared to be a conceptual problem. In dry summer conditions, transpiration was seriously underestimated. The bias in surface runoff and percolation was not of the same sign for all three locations. A sensitivity analysis, set up to explore the impact of using standard parameter values, found that implementing specific soil hydraulic properties had a significant effect on runoff and percolation at all three sites. Evapotranspiration, however affected only slightly at the temperate humid climate sites. Under semi-arid conditions, introducing site specific soil hydraulic properties plus a realistic rooting depth improved simulation results considerably. Future adjustments to the standard LSM should focus on parameter values of soil hydraulic functions and rooting depths and, conceptually, on the bare soil evaporation parameterisation and the soil bottom boundary condition. Implications of changing soil hydraulic properties for future large-simulations were explored briefly. For Europe, soil data requirements can be fulfilled partly by the recent data base HYPRES. Sandy and loamy sand soils will then cover about 65% of Europe, whereas in the present model 100% of the area is loam. Keywords: land surface model; soil hydraulic properties; water balance simulation

As part of the preparation for the Large-Scale Biosphere Atmosphere Experiment in Amazonia, a meso-scale modelling study was executed to highlight deficiencies in the current understanding of land surface atmosphere interaction at local to sub-continental scales in the dry season. Meso-scale models were run in 1-D and 3-D mode for the area of Rondonia State, Brazil. The important conclusions are that without calibration it is difficult to model the energy partitioning of pasture; modelling that of forest is easier due to the absence of a strong moisture deficit signal. The simulation of the boundary layer above forest is good, above deforested areas (pasture) poor. The models' underestimate of the temperature of the boundary layer is likely to be caused by the neglect of the radiative effects of aerosols caused by biomass burning, but other factors such as lack of sufficient entrainment in the model at the mixed layer top may also contribute. The Andes generate patterns of subsidence and gravity waves, the effects of which are felt far into the Rondonian area The results show that the picture presented by GCM modelling studies may need to be balanced by an increased understanding of what happens at the meso-scale. The results are used to identify key measurements for the LBA atmospheric meso-scale campaign needed to improve the model simulations. Similar modelling studies are proposed for the wet season in Rondonia, when convection plays a major role.Key words. Atmospheric composition and structure (aerosols and particles; biosphere-atmosphere interactions) · Meterology and atmospheric dynamics (mesoscale meterology)

No abstract available.

The Hydrologic Atmospheric Pilot EXperiment in the Sahel (HAPEX-Sahel) was carried out in Niger, West Africa, during 1991 - 1992, with an intensive observation period (IOP) in August - October 1992. It aims at improving the parameterization of land surface atmosphere interactions at the Global Circulation Model (GCM) gridbox scale. The experiment combines remote sensing and ground based measurements with hydrological and meteorological modelling to develop aggregation techniques for use in large scale estimates of the hydrological and meteorological behaviour of large areas in the Sahel. The experimental strategy consisted of a period of intensive measurements during the transition period of the rainy to the dry season, backed up by a series of long term measurements in a 1° by 1° square in Niger. Three "supersites" were instrumented with a variety of hydrological and (micro) meteorological equipment to provide detailed information on the surface energy exchange at the local scale. Boundary layer measurements and aircraft measurements were used to provide information at scales of 100 - 500 km2. All relevant remote sensing images were obtained for this period. This programme of measurements is now being analyzed and an extensive modelling programme is under way to aggregate the information at all scales up to the GCM grid box scale. The experimental strategy and some preliminary results of the IOP are described.

There is no current population estimate for Risso’s dolphin (Grampus griseus) in Scottish waters and their distribution in the northeast of Scotland and the Northern Isles is poorly documented. Using a combination of citizen science and historical sightings data, photographs of Risso’s dolphins from northeast Scotland and the Northern Isles were gathered to gain an indication of the presence of the species in this region. A review of all available sighting records from as early as 1933 (n = 1,904 records), showed a historical presence of Risso’s dolphins in the study locations, with a notable increase in sightings reports over time and with higher numbers of sightings reported from July-September. Collation of photographs (n = 1,884) taken by members of the public and researchers facilitated the establishment of two photo-identification catalogues for Northeast Scotland and Orkney (n = 112 individuals) and for Shetland (n = 47 individuals) respectively. Mother/calf pairs were recorded between April and November. Repeated recaptures of identifiable animals throughout the study area provided the first evidence of inter/intra-annual site fidelity. The combined datasets demonstrate the potential importance of inshore coastal waters in northeast Scotland, Orkney, and Shetland as critical habitat for Risso’s dolphins. Recommendations are made for ongoing monitoring studies to inform the protection of Risso’s dolphins in Scottish waters.

Thyroid eye disease (TED) remains challenging for clinicians to evaluate and manage. Novel therapies have recently emerged, and their specific roles are still being determined. Most patients with TED develop eye manifestations while being treated for hyperthyroidism and under the care of endocrinologists. Endocrinologists, therefore, have a key role in diagnosis, initial management, and selection of patients who require referral to specialist care. Given that the need for guidance to endocrinologists charged with meeting the needs of patients with TED transcends national borders, and to maximize an international exchange of knowledge and practices, the American Thyroid Association and European Thyroid Association joined forces to produce this Consensus Statement.

The Hydrologic Atmospheric Pilot EXperiment in the Sahel (HAPEX-Sahel) was carried out in Niger, West Africa, during 1991 - 1992, with an intensive observation period (IOP) in August - October 1992. It aims at improving the parameterization of land surface atmosphere interactions at the Global Circulation Model (GCM) gridbox scale. The experiment combines remote sensing and ground based measurements with hydrological and meteorological modelling to develop aggregation techniques for use in large scale estimates of the hydrological and meteorological behaviour of large areas in the Sahel. The experimental strategy consisted of a period of intensive measurements during the transition period of the rainy to the dry season, backed up by a series of long term measurements in a 1° by 1° square in Niger. Three "supersites" were instrumented with a variety of hydrological and (micro) meteorological equipment to provide detailed information on the surface energy exchange at the local scale. Boundary layer measurements and aircraft measurements were used to provide information at scales of 100 - 500 km2. All relevant remote sensing images were obtained for this period. This programme of measurements is now being analyzed and an extensive modelling programme is under way to aggregate the information at all scales up to the GCM grid box scale. The experimental strategy and some preliminary results of the IOP are described.