Vegetation distribution and state have been measured since 1981 by the AVHRR (Advanced Very High Resolution Radiometer) instrument through satellite remote sensing. In this study a correction method ...is applied to the Pathfinder NDVI (Normalized Difference Vegetation Index) data to create a continuous European vegetation phenology dataset of a 10-day temporal and 0.1° spatial resolution; additionally, land surface parameters for use in biosphere-atmosphere modelling are derived. The analysis of time-series from this dataset reveals, for the years 1982-2001, strong seasonal and interannual variability in European land surface vegetation state. Phenological metrics indicate a late and short growing season for the years 1985-1987, in addition to early and prolonged activity in the years 1989, 1990, 1994 and 1995. These variations are in close agreement with findings from phenological measurements at the surface; spring phenology is also shown to correlate particularly well with anomalies in winter temperature and winter North Atlantic Oscillation (NAO) index. Nevertheless, phenological metrics, which display considerable regional differences, could only be determined for vegetation with a seasonal behaviour. Trends in the phenological phases reveal a general shift to earlier (−0.54 days year
−1
) and prolonged (0.96 days year
−1
) growing periods which are statistically significant, especially for central Europe.
The Community Land Model version 3 (CLM3) is the land component of the Community Climate System Model (CCSM). CLM3 has energy and water biases resulting from deficiencies in some of its canopy and ...soil parameterizations related to hydrological processes. Recent research by the community that utilizes CLM3 and the family of CCSM models has indicated several promising approaches to alleviating these biases. This paper describes the implementation of a selected set of these parameterizations and their effects on the simulated hydrological cycle. The modifications consist of surface data sets based on Moderate Resolution Imaging Spectroradiometer products, new parameterizations for canopy integration, canopy interception, frozen soil, soil water availability, and soil evaporation, a TOPMODEL‐based model for surface and subsurface runoff, a groundwater model for determining water table depth, and the introduction of a factor to simulate nitrogen limitation on plant productivity. The results from a set of offline simulations were compared with observed data for runoff, river discharge, soil moisture, and total water storage to assess the performance of the new model (referred to as CLM3.5). CLM3.5 exhibits significant improvements in its partitioning of global evapotranspiration (ET) which result in wetter soils, less plant water stress, increased transpiration and photosynthesis, and an improved annual cycle of total water storage. Phase and amplitude of the runoff annual cycle is generally improved. Dramatic improvements in vegetation biogeography result when CLM3.5 is coupled to a dynamic global vegetation model. Lower than observed soil moisture variability in the rooting zone is noted as a remaining deficiency.
▶ Analysis of EB deficit in multi-site analysis with up to 26 sites. ▶ Relative EB deficit is larger for very unstable conditions than for less unstable conditions, because of reduced ...mechanically-induced turbulence for very unstable conditions, and not related with increased thermally-induced turbulence. ▶ Average absolute EB deficits are largest when relative EB deficits are smallest. ▶ Relative EB deficits are smallest for strong thermally-induced turbulence (TT) or strongly suppressed TT, but largest for intermediate conditions. ▶ For non-neutral atmospheric conditions the relative EB deficit is not given by the slope of the regression line of the absolute EB deficit as a function of net radiation. ▶ Storage terms are important for reducing relative EB deficit during nighttime and stable conditions, but otherwise not so important.
This paper presents a multi-site (>20) analysis of the relative and absolute energy balance (EB) closure at European FLUXNET sites, as a function of the stability parameter
ξ, the friction velocity
u
*, thermally-induced turbulence, and the time of the day. A focus of the analysis is the magnitude of EB deficits for very unstable conditions. A univariate analysis of the relative EB deficit as function of
ξ alone (both for individual sites and a synthesis for all sites), reveals that the relative EB deficit is larger for very unstable conditions (
ξ
<
−1.0) than for less unstable conditions (−0.02
>
ξ
≥
−1.0). A bivariate analysis of the relative EB deficit as function of both
ξ and
u
*, however, indicates that for situations with comparable
u
* the closure is better for very unstable conditions than for less unstable conditions. Our results suggest that the poorer closure for very unstable conditions identified from the univariate analysis is due to reduced
u
* under these conditions. In addition, we identify that the conditions characterized by smallest relative EB deficits (elevated overall turbulence, mostly during day time) correspond to cases with the largest absolute EB deficits. Thus, the total EB deficit at the sites is induced mostly under these conditions, which is particularly relevant for evapotranspiration estimates. Further, situations with the largest relative EB deficits are generally characterized by small absolute EB deficits. We also find that the relative EB deficit does generally not correspond to the regression line of absolute EB deficit with the net radiation because there is a (positive or negative) offset. This can be understood from theoretical considerations. Finally, we find that storage effects explain a considerable fraction of the large
relative (but small absolute) nocturnal EB deficits, and only a limited fraction of the overall relative and absolute EB deficits.
Solar surface irradiance (SIS) and direct (SID) irradiance as well as effective cloud albedo (CAL) climate data records (CDR) derived from the Meteosat first generation satellites (Meteosats 2 to 7, ...1983–2005) are presented. The CDRs are available free of charge for all purposes from wui.cmsaf.eu at monthly, daily and hourly means at a spatial resolution of 0.03∘.
The processing employed a climate version of the Heliosat algorithm combined with a clear sky model using an eigenvector look-up table method. Modifications to the Heliosat method include a self-calibration algorithm as well as a running mean based clear sky retrieval algorithm.
The datasets are validated using ground based observations from the Baseline Surface Radiation Network (BSRN) as a reference. The validation threshold for the mean absolute bias between satellite-derived and surface-measured radiation is given by the target accuracy for solar irradiance fields defined by the Global Climate Observing system (GCOS) and a measurement uncertainty for the surface data. The results demonstrate that the target accuracy is achieved for monthly and daily means. Furthermore, an intercomparison with similar datasets reveal a better performance and climate monitoring potential of the CM SAF SIS CDR at most BSRN sites compared to established data sets like e.g. ERA-reanalysis, GEWEX (Global Energy and Water Cycle Experiment) and ISCCP (International Satellite Cloud Climatology Project). Lastly, the realistic representation of both seasonal and inter-annual variability guarantees the applicability of the satellite-based climate data sets for climate monitoring and analysis of extremes.
No trends in the normalized bias between the CM SAF and the BSRN datasets are detectable, which demonstrates the stability and homogeneity of the global and direct irradiance for the period covered by BSRN measurements. However, inconsistencies are detectable at few satellite transition dates for certain regions in earlier years.
► Remote Sensing of solar surface radiation and effective cloud albedo. ► Improvement and evaluation of retrieval methods. ► Presentation of a new self-calibration method. ► Retrieval of planetary clear sky reflection. including snow masking. ► Assesment of satellite based solar irradiance data sets.
Predicting the global carbon and water cycle requires a realistic representation of vegetation phenology in climate models. However most prognostic phenology models are not yet suited for global ...applications, and diagnostic satellite data can be uncertain and lack predictive power. We present a framework for data assimilation of Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) and Leaf Area Index (LAI) from the MODerate Resolution Imaging Spectroradiometer (MODIS) to constrain empirical temperature, light, moisture and structural vegetation parameters of a prognostic phenology model. We find that data assimilation better constrains structural vegetation parameters than climate control parameters. Improvements are largest for drought‐deciduous ecosystems where correlation of predicted versus satellite‐observed FPAR and LAI increases from negative to 0.7–0.8. Data assimilation effectively overcomes the cloud‐ and aerosol‐related deficiencies of satellite data sets in tropical areas. Validation with a 49‐year‐long phenology data set reveals that the temperature‐driven start of season (SOS) is light limited in warm years. The model has substantial skill (R = 0.73) to reproduce SOS inter‐annual and decadal variability. Predicted SOS shows a higher inter‐annual variability with a negative bias of 5–20 days compared to species‐level SOS. It is however accurate to within 1–2 days compared to SOS derived from net ecosystem exchange (NEE) measurements at a FLUXNET tower. The model only has weak skill to predict end of season (EOS). Use of remote sensing data assimilation for phenology model development is encouraged but validation should be extended with phenology data sets covering mediterranean, tropical and arctic ecosystems.
The Community Land Model version 3 (CLM3.0) simulates land‐atmosphere exchanges in response to climatic forcings. CLM3.0 has known biases in the surface energy partitioning as a result of ...deficiencies in its hydrological and biophysical parameterizations. Such models, however, need to be robust for multidecadal global climate simulations. FLUXNET now provides an extensive data source of carbon, water and energy exchanges for investigating land processes, and it encompasses a global range of ecosystem‐climate interactions. Data from 15 FLUXNET sites are used to identify and improve model deficiencies. Including a prognostic aquifer, a bare soil evaporation resistance formulation and numerous other changes in the model result in a significantly improved soil hydrology and energy partitioning. Terrestrial water storage increased by up to 300 mm in warm climates and decreased in cold climates. Nitrogen control of photosynthesis is revealed as another missing process in the model. These improvements increase the correlation coefficient of hourly and monthly latent heat fluxes from a range of 0.5–0.6 to the range of 0.7–0.9. RMSE of the simulated sensible heat fluxes decrease by 20–50%. Primary production is overestimated during the wet season in mediterranean and tropical ecosystems. This might be related to missing carbon‐nitrogen dynamics as well as to site‐specific parameters. The new model (CLM3.5) with an improved terrestrial water cycle should lead to more realistic land‐atmosphere exchanges in coupled simulations. FLUXNET is found to be a valuable tool to develop and validate land surface models prior to their application in computationally expensive global simulations.
Grasslands grow in a sequence of seasonal growth stages that respond to both climate and weather, and these relationships can be used to establish a strategy for predicting plant phenology. Current ...plant states (phenophase) can be represented as one of established growth stages that dictate carbon allocation and leaf photosynthetic capacity. Calculating daily phenophases from climate and environmental relationships allows for sequential growth stages (i.e., well‐defined seasonal cycles with a single growth period) or dynamic growth stages (i.e., multiple growth periods during a growing season). Senescence results from biomass mortality in response to environmental conditions. This approach uses a single mechanistic framework to represent grassland ecology, removing the dependence on satellite‐based vegetation indices and individual site tuning of parameters. Rather than being specified, a variety of properties emerge, from allometric relationships such as root‐shoot ratios, to behavior across moisture gradients, to interannual variability in growing season lengths, carbon stores, and land surface fluxes. Using dynamic phenology stages to link biophysical and biogeochemical processes provides a mechanism to predict self‐consistent land‐atmosphere exchanges of carbon, water, energy, radiation, and momentum, as well as carbon storage in cascading pools of biomass; and describing these processes in a mathematically determinate model makes them clear, testable, and usable for predictions. This paper describes this new phenology method as it is implemented in the Simple Biosphere Model Version 4 (SiB4), and a companion paper evaluates this method at grassland sites worldwide.
Plain Language Summary
Every year, grasslands grow in a sequence of seasonal growth stages that have evolved in nature. For grasslands, climate and weather patterns can be used to establish a strategy for predicting these stages. Using five different possible stages, the current stage can be used to determine which part of the plant grows the fastest. This approach uses a single set of equations to represent grassland ecology, removing the dependence on satellite‐based vegetation information. Rather than being specified, a variety of properties emerge, such as different growth patterns in regions that receive more rainfall and different growth rates per year. This approach links plant processes and provides a way to model plant growth and its interaction with the atmosphere. This paper describes this new method as it is implemented in the Simple Biosphere Model Version 4 and provides an example at a grassland site.
Key Points
Grassland phenology can be predicted using a strategy based on growth stages that respond to climate and weather
Dynamic prognostic phenology links biophysical and biogeochemical processes to predict the terrestrial carbon cycle
This study evaluates the suitability of the method HelioMont, developed by MeteoSwiss, for estimating solar radiation from geostationary satellite data over the Alpine region. The algorithm accounts ...for the influence of topography, clouds, snow cover and the atmosphere on incoming solar radiation. The main error sources are investigated for both direct and diffuse solar radiation components by comparison with ground-based measurement taken at three sites, namely Bolzano (IT), Davos (CH) and Payerne (CH), encompassing different topographic conditions. The comparison shows that the method provides high accuracy of the yearly cycle: the Mean Absolute Bias (MAB) is below 5Wm−2 at the lowland station Payerne and below 12Wm−2 at the other two mountainous stations for the monthly averages of global and diffuse radiation. For diffuse radiation the MAB is in the range 11–15Wm−2 for daily means and 34–40Wm−2 for hourly means. It is found that the largest errors in diffuse and direct radiation components on shorter time scales occur during summer and for cloud-free days. In both Bolzano and Davos the errors for daily-mean diffuse radiation can exceed 50Wm−2 under such conditions. As HelioMont uses monthly climatological values of atmospheric aerosol characteristics, the effects of this approximation are investigated by simulating clear-sky solar radiation with the radiative transfer model (RTM) libRadtran using instantaneous aerosol measurements. Both ground-based and satellite-based data on aerosol optical properties and water vapor column amount are evaluated. When using daily atmospheric input the estimation of the hourly averages improves significantly and the mean error is reduced to 10–20Wm−2. These results suggest the need for a more detailed characterization of the local-scale clear-sky atmospheric conditions for modeling solar radiation on daily and hourly time scales.
•We validate HelioMont, an algorithm retrieving surface radiation from satellite data.•We focus on three sites in the Alps, encompassing different topographic conditions.•The estimation error for radiation components is strong under cloud free conditions.•Using monthly climatological values of aerosol characteristics has a big impact.•Better results are obtained with daily local-scale aerosol input.
Satellite‐based, long‐term records of surface albedo characterization that accurately capture spatial and temporal patterns are essential to develop climate models and to monitor the impact of land ...use changes on the terrestrial energy and water balance. This study presents the first Bidirectional Reflectance Distribution Function (BRDF) and albedo data set derived from the Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage reflectance data acquired on board National Oceanic and Atmospheric Administration and Meteorological Operational platforms from 1990 to 2014 over Europe. The objectives of this paper are to describe the data set's surface albedo climatology and anomalies in the visible, near‐infrared, and shortwave broadbands for the growing season months of May to September in order to facilitate utilization of the data by the climate modeling communities. The results demonstrate that the AVHRR BRDF and albedo data have temporal and spatial patterns that are appropriate for the underlying predominant land cover type and accurately reflect the associated climate variation. Visible and near‐infrared broadband albedo anomalies are found to be contrasting in most years, and their spatial distributions depict responses of vegetation to climate events (e.g., heat waves). Visible albedo of crops and near‐infrared albedo of pastures show a higher interannual variation than respective albedos of other snow‐free land covers, while the interannual standard deviations are found to be lower than 0.015. Our findings indicate the importance of taking into account the spectrally distinct variability of surface albedo when analyzing its complex spatiotemporal dynamics in climate‐related research.
Key Points
The first 1 km resolution AVHRR BRDF and albedo data set from 1990 to 2014 for Europe is presented
Analysis of shortwave albedo anomalies is insufficient as albedo anomalies in VIS and NIR are mostly contrasting
Climate variation and vegetation response are reflected in spectrally distinct spatiotemporal albedo patterns
PDF
