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- Author or Editor: Gianpaolo Balsamo x
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Abstract
The Community Microwave Emission Model (CMEM) has been used to compute global L-band brightness temperatures at the top of the atmosphere. The input data comprise surface fields from the 40-yr ECMWF Re-Analysis (ERA-40), vegetation data from the ECOCLIMAP dataset, and the Food and Agriculture Organization’s (FAO) soil database. Modeled brightness temperatures have been compared against (historic) observations from the S-194 passive microwave radiometer onboard the Skylab space station. Different parameterizations for surface roughness and the vegetation optical depth have been used to calibrate the model. The best results have been obtained for rather simple approaches proposed by Wigneron et al. and Kirdyashev et al. The rms errors after calibration are 10.7 and 9.8 K for North and South America, respectively. Comparing the ERA-40 soil moisture product against the corresponding in situ observations suggests that the uncertainty in the modeled soil moisture is the predominant contributor to these rms errors. Although the bias between model and observed brightness temperatures are reduced after the calibration, systematic differences in the dynamic range remain. For NWP analysis applications, bias correction schemes should be applied prior to data assimilation. The calibrated model has been used to compute a 10-yr brightness temperature climatology based on ERA-40 data.
Abstract
The Community Microwave Emission Model (CMEM) has been used to compute global L-band brightness temperatures at the top of the atmosphere. The input data comprise surface fields from the 40-yr ECMWF Re-Analysis (ERA-40), vegetation data from the ECOCLIMAP dataset, and the Food and Agriculture Organization’s (FAO) soil database. Modeled brightness temperatures have been compared against (historic) observations from the S-194 passive microwave radiometer onboard the Skylab space station. Different parameterizations for surface roughness and the vegetation optical depth have been used to calibrate the model. The best results have been obtained for rather simple approaches proposed by Wigneron et al. and Kirdyashev et al. The rms errors after calibration are 10.7 and 9.8 K for North and South America, respectively. Comparing the ERA-40 soil moisture product against the corresponding in situ observations suggests that the uncertainty in the modeled soil moisture is the predominant contributor to these rms errors. Although the bias between model and observed brightness temperatures are reduced after the calibration, systematic differences in the dynamic range remain. For NWP analysis applications, bias correction schemes should be applied prior to data assimilation. The calibrated model has been used to compute a 10-yr brightness temperature climatology based on ERA-40 data.
Abstract
Reanalysis products represent a valuable source of information for different impact modeling and monitoring activities over regions with sparse observational data. It is therefore essential to evaluate their behavior and their intrinsic uncertainties. This study focuses on precipitation over monsoon Asia, a key agricultural region of the world. Four reanalysis datasets are evaluated, namely ERA-Interim, ERA-Interim/Land, AgMERRA (an agricultural version of MERRA), and JRA-55. APHRODITE and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset are the two gridded observational datasets used for the evaluation; the former is based on rain gauge data and the latter on a combination of satellite and rain gauge data. Differences in seasonality, moderate-to-heavy precipitation events, daily distribution, and drought characteristics are analyzed. Results show remarkable differences between the APHRODITE and CHIRPS observational datasets as well as between these datasets and the reanalyses. AgMERRA generally achieves the best performance, but it is not updated at near–real time. ERA-Interim/Land shows good spatial performance, but when the interest is on the temporal evolution JRA-55 is recommended, as it exhibits the most stable temporal behavior. This study shows that the use of reanalyses for impact modeling and monitoring over monsoon Asia requires an accurate evaluation and choices to be tailored to the specific needs.
Abstract
Reanalysis products represent a valuable source of information for different impact modeling and monitoring activities over regions with sparse observational data. It is therefore essential to evaluate their behavior and their intrinsic uncertainties. This study focuses on precipitation over monsoon Asia, a key agricultural region of the world. Four reanalysis datasets are evaluated, namely ERA-Interim, ERA-Interim/Land, AgMERRA (an agricultural version of MERRA), and JRA-55. APHRODITE and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset are the two gridded observational datasets used for the evaluation; the former is based on rain gauge data and the latter on a combination of satellite and rain gauge data. Differences in seasonality, moderate-to-heavy precipitation events, daily distribution, and drought characteristics are analyzed. Results show remarkable differences between the APHRODITE and CHIRPS observational datasets as well as between these datasets and the reanalyses. AgMERRA generally achieves the best performance, but it is not updated at near–real time. ERA-Interim/Land shows good spatial performance, but when the interest is on the temporal evolution JRA-55 is recommended, as it exhibits the most stable temporal behavior. This study shows that the use of reanalyses for impact modeling and monitoring over monsoon Asia requires an accurate evaluation and choices to be tailored to the specific needs.
Abstract
Three different complexity snow schemes implemented in the ECMWF land surface scheme Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) are evaluated within the EC-EARTH climate model. The snow schemes are (i) the original HTESSEL single-bulk-layer snow scheme, (ii) a new snow scheme in operations at ECMWF since September 2009, and (iii) a multilayer version of the previous. In offline site simulations, the multilayer scheme outperforms the single-layer schemes in deep snowpack conditions through its ability to simulate sporadic melting events thanks to the lower thermal inertial of the uppermost layer. Coupled atmosphere–land/snow simulations performed by the EC-EARTH climate model are validated against remote sensed snow cover and surface albedo. The original snow scheme has a systematic early melting linked to an underestimation of surface albedo during spring that was partially reduced with the new snow schemes. A key process to improve the realism of the near-surface atmospheric temperature and at the same time the soil freezing is the thermal insulation of the snowpack (tightly coupled with the accuracy of snow mass and density simulations). The multilayer snow scheme outperforms the single-layer schemes in open deep snowpack (such as prairies or tundra in northern latitudes) and is instead comparable in shallow snowpack conditions. However, the representation of orography in current climate models implies limitations for accurately simulating the snowpack, particularly over complex terrain regions such as the Rockies and the Himalayas.
Abstract
Three different complexity snow schemes implemented in the ECMWF land surface scheme Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL) are evaluated within the EC-EARTH climate model. The snow schemes are (i) the original HTESSEL single-bulk-layer snow scheme, (ii) a new snow scheme in operations at ECMWF since September 2009, and (iii) a multilayer version of the previous. In offline site simulations, the multilayer scheme outperforms the single-layer schemes in deep snowpack conditions through its ability to simulate sporadic melting events thanks to the lower thermal inertial of the uppermost layer. Coupled atmosphere–land/snow simulations performed by the EC-EARTH climate model are validated against remote sensed snow cover and surface albedo. The original snow scheme has a systematic early melting linked to an underestimation of surface albedo during spring that was partially reduced with the new snow schemes. A key process to improve the realism of the near-surface atmospheric temperature and at the same time the soil freezing is the thermal insulation of the snowpack (tightly coupled with the accuracy of snow mass and density simulations). The multilayer snow scheme outperforms the single-layer schemes in open deep snowpack (such as prairies or tundra in northern latitudes) and is instead comparable in shallow snowpack conditions. However, the representation of orography in current climate models implies limitations for accurately simulating the snowpack, particularly over complex terrain regions such as the Rockies and the Himalayas.
Abstract
We apply spectral empirical orthogonal function (SEOF) analysis to educe climate patterns as dominant spatiotemporal modes of variability from reanalysis data. SEOF is a frequency-domain variant of standard empirical orthogonal function (EOF) analysis, and computes modes that represent the statistically most relevant and persistent patterns from an eigendecomposition of the estimated cross-spectral density matrix (CSD). The spectral estimation step distinguishes the approach from other frequency-domain EOF methods based on a single realization of the Fourier transform, and results in a number of desirable mathematical properties: at each frequency, SEOF yields a set of orthogonal modes that are optimally ranked in terms of variance in the L 2 sense, and that are coherent in both space and time by construction. We discuss the differences between SEOF and other competing approaches, as well as its relation to dynamical modes of stochastically forced, nonnormal linear dynamical systems. The method is applied to ERA-Interim and ERA-20C reanalysis data, demonstrating its ability to identify a number of well-known spatiotemporal coherent meteorological patterns and teleconnections, including the Madden–Julian oscillation (MJO), the quasi-biennial oscillation (QBO), and the El Niño–Southern Oscillation (ENSO) (i.e., a range of phenomena reoccurring with average periods ranging from months to many years). In addition to two-dimensional univariate analyses of surface data, we give examples of multivariate and three-dimensional meteorological patterns that illustrate how this technique can systematically identify coherent structures from different sets of data. The MATLAB code used to compute the results presented in this study, including the download scripts for the reanalysis data, is freely available online.
Abstract
We apply spectral empirical orthogonal function (SEOF) analysis to educe climate patterns as dominant spatiotemporal modes of variability from reanalysis data. SEOF is a frequency-domain variant of standard empirical orthogonal function (EOF) analysis, and computes modes that represent the statistically most relevant and persistent patterns from an eigendecomposition of the estimated cross-spectral density matrix (CSD). The spectral estimation step distinguishes the approach from other frequency-domain EOF methods based on a single realization of the Fourier transform, and results in a number of desirable mathematical properties: at each frequency, SEOF yields a set of orthogonal modes that are optimally ranked in terms of variance in the L 2 sense, and that are coherent in both space and time by construction. We discuss the differences between SEOF and other competing approaches, as well as its relation to dynamical modes of stochastically forced, nonnormal linear dynamical systems. The method is applied to ERA-Interim and ERA-20C reanalysis data, demonstrating its ability to identify a number of well-known spatiotemporal coherent meteorological patterns and teleconnections, including the Madden–Julian oscillation (MJO), the quasi-biennial oscillation (QBO), and the El Niño–Southern Oscillation (ENSO) (i.e., a range of phenomena reoccurring with average periods ranging from months to many years). In addition to two-dimensional univariate analyses of surface data, we give examples of multivariate and three-dimensional meteorological patterns that illustrate how this technique can systematically identify coherent structures from different sets of data. The MATLAB code used to compute the results presented in this study, including the download scripts for the reanalysis data, is freely available online.
Abstract
A new snow scheme for the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model has been tested and validated. The scheme includes a new parameterization of snow density, incorporating a liquid water reservoir, and revised formulations for the subgrid snow cover fraction and snow albedo. Offline validation (covering a wide range of spatial and temporal scales) includes simulations for several observation sites from the Snow Models Intercomparison Project-2 (SnowMIP2) and global simulations driven by the meteorological forcing from the Global Soil Wetness Project-2 (GSWP2) and by ECMWF Re-Analysis ERA-Interim. The new scheme reduces the end of season ablation biases from 10 to 2 days in open areas and from 21 to 13 days in forest areas. Global GSWP2 results are compared against basin-scale runoff and terrestrial water storage. The new snow density parameterization increases the snow thermal insulation, reducing soil freezing and leading to an improved hydrological cycle. Simulated snow cover fraction is compared against NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) with a reduction of the negative bias of snow-covered area of the original snow scheme. The original snow scheme had a systematic negative bias in surface albedo when compared against Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. The new scheme reduces the albedo bias, consequently reducing the spatial- and time-averaged surface net shortwave radiation bias by 5.2 W m−2 in 14% of the Northern Hemisphere land. The new snow scheme described in this paper was introduced in the ECMWF operational forecast system in September 2009 (cycle 35R3).
Abstract
A new snow scheme for the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model has been tested and validated. The scheme includes a new parameterization of snow density, incorporating a liquid water reservoir, and revised formulations for the subgrid snow cover fraction and snow albedo. Offline validation (covering a wide range of spatial and temporal scales) includes simulations for several observation sites from the Snow Models Intercomparison Project-2 (SnowMIP2) and global simulations driven by the meteorological forcing from the Global Soil Wetness Project-2 (GSWP2) and by ECMWF Re-Analysis ERA-Interim. The new scheme reduces the end of season ablation biases from 10 to 2 days in open areas and from 21 to 13 days in forest areas. Global GSWP2 results are compared against basin-scale runoff and terrestrial water storage. The new snow density parameterization increases the snow thermal insulation, reducing soil freezing and leading to an improved hydrological cycle. Simulated snow cover fraction is compared against NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) with a reduction of the negative bias of snow-covered area of the original snow scheme. The original snow scheme had a systematic negative bias in surface albedo when compared against Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data. The new scheme reduces the albedo bias, consequently reducing the spatial- and time-averaged surface net shortwave radiation bias by 5.2 W m−2 in 14% of the Northern Hemisphere land. The new snow scheme described in this paper was introduced in the ECMWF operational forecast system in September 2009 (cycle 35R3).
Abstract
The tiling method is used in many land surface models to represent the surface heterogeneity. Each grid box is divided into fractions of different types of land use with independent solutions of the surface energy budget. An area-weighted average of the energy fluxes is computed to couple with the atmosphere, assuming the air above the surface is well blended at a given height. In the framework of validation of the tiling method, the ECMWF land surface scheme has been tested in offline mode driven by meteorological forcing provided by the ECMWF Interim Re-Analysis (ERA-Interim). Two contrasting surfaces in the boreal region of southern Finland are considered: a Scots pine forest (Hyytiälä) and a small nearby lake (Valkea-Kotinen). The field observations are used to evaluate the land surface model simulations for both energy fluxes and reservoirs. The model is able to characterize the main difference between the two sites, which appears in the energy partitioning, explained by the lake’s large thermal inertia. In fact, while a large portion of the incoming solar radiation in the forest is released as sensible heat, the lake stores a substantial amount of energy in the water during late spring and summer and releases it in autumn. The different behavior of the fluxes both on annual and diurnal time scales confirms the benefit of a tiling mechanism in the presence of large contrast. However, it is also shown that the assumption of similar conditions at the blending height introduces errors.
Abstract
The tiling method is used in many land surface models to represent the surface heterogeneity. Each grid box is divided into fractions of different types of land use with independent solutions of the surface energy budget. An area-weighted average of the energy fluxes is computed to couple with the atmosphere, assuming the air above the surface is well blended at a given height. In the framework of validation of the tiling method, the ECMWF land surface scheme has been tested in offline mode driven by meteorological forcing provided by the ECMWF Interim Re-Analysis (ERA-Interim). Two contrasting surfaces in the boreal region of southern Finland are considered: a Scots pine forest (Hyytiälä) and a small nearby lake (Valkea-Kotinen). The field observations are used to evaluate the land surface model simulations for both energy fluxes and reservoirs. The model is able to characterize the main difference between the two sites, which appears in the energy partitioning, explained by the lake’s large thermal inertia. In fact, while a large portion of the incoming solar radiation in the forest is released as sensible heat, the lake stores a substantial amount of energy in the water during late spring and summer and releases it in autumn. The different behavior of the fluxes both on annual and diurnal time scales confirms the benefit of a tiling mechanism in the presence of large contrast. However, it is also shown that the assumption of similar conditions at the blending height introduces errors.
Abstract
In the last decade operational probabilistic ensemble flood forecasts have become common in supporting decision-making processes leading to risk reduction. Ensemble forecasts can assess uncertainty, but they are limited to the uncertainty in a specific modeling system. Many of the current operational flood prediction systems use a multimodel approach to better represent the uncertainty arising from insufficient model structure. This study presents a multimodel approach to building a global flood prediction system using multiple atmospheric reanalysis datasets for river initial conditions and multiple TIGGE forcing inputs to the ECMWF land surface model. A sensitivity study is carried out to clarify the effect of using archive ensemble meteorological predictions and uncoupled land surface models. The probabilistic discharge forecasts derived from the different atmospheric models are compared with those from the multimodel combination. The potential for further improving forecast skill by bias correction and Bayesian model averaging is examined. The results show that the impact of the different TIGGE input variables in the HTESSEL/Catchment-Based Macroscale Floodplain model (CaMa-Flood) setup is rather limited other than for precipitation. This provides a sufficient basis for evaluation of the multimodel discharge predictions. The results also highlight that the three applied reanalysis datasets have different error characteristics that allow for large potential gains with a multimodel combination. It is shown that large improvements to the forecast performance for all models can be achieved through appropriate statistical postprocessing (bias and spread correction). A simple multimodel combination generally improves the forecasts, while a more advanced combination using Bayesian model averaging provides further benefits.
Abstract
In the last decade operational probabilistic ensemble flood forecasts have become common in supporting decision-making processes leading to risk reduction. Ensemble forecasts can assess uncertainty, but they are limited to the uncertainty in a specific modeling system. Many of the current operational flood prediction systems use a multimodel approach to better represent the uncertainty arising from insufficient model structure. This study presents a multimodel approach to building a global flood prediction system using multiple atmospheric reanalysis datasets for river initial conditions and multiple TIGGE forcing inputs to the ECMWF land surface model. A sensitivity study is carried out to clarify the effect of using archive ensemble meteorological predictions and uncoupled land surface models. The probabilistic discharge forecasts derived from the different atmospheric models are compared with those from the multimodel combination. The potential for further improving forecast skill by bias correction and Bayesian model averaging is examined. The results show that the impact of the different TIGGE input variables in the HTESSEL/Catchment-Based Macroscale Floodplain model (CaMa-Flood) setup is rather limited other than for precipitation. This provides a sufficient basis for evaluation of the multimodel discharge predictions. The results also highlight that the three applied reanalysis datasets have different error characteristics that allow for large potential gains with a multimodel combination. It is shown that large improvements to the forecast performance for all models can be achieved through appropriate statistical postprocessing (bias and spread correction). A simple multimodel combination generally improves the forecasts, while a more advanced combination using Bayesian model averaging provides further benefits.
Abstract
The Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) is used operationally in the Integrated Forecast System (IFS) for describing the evolution of soil, vegetation, and snow over the continents at diverse spatial resolutions. A revised land surface hydrology (H-TESSEL) is introduced in the ECMWF operational model to address shortcomings of the land surface scheme, specifically the lack of surface runoff and the choice of a global uniform soil texture. New infiltration and runoff schemes are introduced with a dependency on the soil texture and standard deviation of orography. A set of experiments in stand-alone mode is used to assess the improved prediction of soil moisture at the local scale against field site observations. Comparison with basin-scale water balance (BSWB) and Global Runoff Data Centre (GRDC) datasets indicates a consistently larger dynamical range of land water mass over large continental areas and an improved prediction of river runoff, while the effect on atmospheric fluxes is fairly small. Finally, the ECMWF data assimilation and prediction systems are used to verify the effect on surface and near-surface quantities in the atmospheric-coupled mode. A midlatitude error reduction is seen both in soil moisture and in 2-m temperature.
Abstract
The Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) is used operationally in the Integrated Forecast System (IFS) for describing the evolution of soil, vegetation, and snow over the continents at diverse spatial resolutions. A revised land surface hydrology (H-TESSEL) is introduced in the ECMWF operational model to address shortcomings of the land surface scheme, specifically the lack of surface runoff and the choice of a global uniform soil texture. New infiltration and runoff schemes are introduced with a dependency on the soil texture and standard deviation of orography. A set of experiments in stand-alone mode is used to assess the improved prediction of soil moisture at the local scale against field site observations. Comparison with basin-scale water balance (BSWB) and Global Runoff Data Centre (GRDC) datasets indicates a consistently larger dynamical range of land water mass over large continental areas and an improved prediction of river runoff, while the effect on atmospheric fluxes is fairly small. Finally, the ECMWF data assimilation and prediction systems are used to verify the effect on surface and near-surface quantities in the atmospheric-coupled mode. A midlatitude error reduction is seen both in soil moisture and in 2-m temperature.
Abstract
Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those it is found that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely because of differences in instrumentation, calibration, and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat-dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory), and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but they poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration, or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison.
Abstract
Four land surface models in uncoupled and coupled configurations are compared to observations of daily soil moisture from 19 networks in the conterminous United States to determine the viability of such comparisons and explore the characteristics of model and observational data. First, observations are analyzed for error characteristics and representation of spatial and temporal variability. Some networks have multiple stations within an area comparable to model grid boxes; for those it is found that aggregation of stations before calculation of statistics has little effect on estimates of variance, but soil moisture memory is sensitive to aggregation. Statistics for some networks stand out as unlike those of their neighbors, likely because of differences in instrumentation, calibration, and maintenance. Buried sensors appear to have less random error than near-field remote sensing techniques, and heat-dissipation sensors show less temporal variability than other types. Model soil moistures are evaluated using three metrics: standard deviation in time, temporal correlation (memory), and spatial correlation (length scale). Models do relatively well in capturing large-scale variability of metrics across climate regimes, but they poorly reproduce observed patterns at scales of hundreds of kilometers and smaller. Uncoupled land models do no better than coupled model configurations, nor do reanalyses outperform free-running models. Spatial decorrelation scales are found to be difficult to diagnose. Using data for model validation, calibration, or data assimilation from multiple soil moisture networks with different types of sensors and measurement techniques requires great caution. Data from models and observations should be put on the same spatial and temporal scales before comparison.
Abstract
This study compares four model systems in three configurations (LSM, LSM + GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly underrepresent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land–atmosphere coupling) and may overrepresent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally underrepresent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly, and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Although the models validate better against Bowen-ratio-corrected surface flux observations, which allow for closure of surface energy balances at flux tower sites, it is not clear whether the corrected fluxes are more representative of actual fluxes. The analysis illuminates targets for coupled land–atmosphere model development, as well as the value of long-term globally distributed observational monitoring.
Abstract
This study compares four model systems in three configurations (LSM, LSM + GCM, and reanalysis) with global flux tower observations to validate states, surface fluxes, and coupling indices between land and atmosphere. Models clearly underrepresent the feedback of surface fluxes on boundary layer properties (the atmospheric leg of land–atmosphere coupling) and may overrepresent the connection between soil moisture and surface fluxes (the terrestrial leg). Models generally underrepresent spatial and temporal variability relative to observations, which is at least partially an artifact of the differences in spatial scale between model grid boxes and flux tower footprints. All models bias high in near-surface humidity and downward shortwave radiation, struggle to represent precipitation accurately, and show serious problems in reproducing surface albedos. These errors create challenges for models to partition surface energy properly, and errors are traceable through the surface energy and water cycles. The spatial distribution of the amplitude and phase of annual cycles (first harmonic) are generally well reproduced, but the biases in means tend to reflect in these amplitudes. Interannual variability is also a challenge for models to reproduce. Although the models validate better against Bowen-ratio-corrected surface flux observations, which allow for closure of surface energy balances at flux tower sites, it is not clear whether the corrected fluxes are more representative of actual fluxes. The analysis illuminates targets for coupled land–atmosphere model development, as well as the value of long-term globally distributed observational monitoring.
