[1] Simulations of the global water and carbon cycle are sensitive to the model representation of vegetation phenology. Current phenology models are empirical, and few predict both phenological timing and leaf state. Our previous study demonstrated how satellite data assimilation employing an Ensemble Kalman Filter yields realistic phenological model parameters for several ecosystem types. In this study the data assimilation framework is extended to global scales using a subgrid‐scale representation of plant functional types (PFTs) and elevation classes. A reanalysis of vegetation phenology for 256 globally distributed regions is performed using 10 years of Moderate Resolution Imaging Spectroradiometer (MODIS) fraction of photosynthetically active radiation (FPAR) absorbed by vegetation and leaf area index (LAI) data. The 9 · 10⁸ quality screened observations (corresponding to <1% of the globally available MODIS data) successfully constrain a posterior PFT‐dependent phenological parameter set. It reduces the global FPAR and LAI prediction error to 20.6% and 14.8%, respectively, compared to the prior prediction error. A 50 year long (1960–2009) daily 1° × 1° global phenology data set with a mean FPAR and LAI prediction error of 0.065 (−) and 0.34 (m² m⁻²) is generated. Temperate phenology is best explained by a combination of light and temperature. Tropical evergreen phenology is found to be largely insensitive to moisture and light variations. Boreal phenology can be accurately predicted from local to global scales, while temperate and mediterranean landscapes might benefit from a better subgrid‐scale PFT classification or from a more complex canopy radiative transfer model.

The European Organization for the Exploitation of Meteorological Satellites’ (EUMETSAT) Meteosat satellites provide the unique opportunity to compile a 30+ year land surface temperature (LST) climate data record. Since the Meteosat instrument on-board Meteosat 2–7 is equipped with a single thermal channel, single-channel LST retrieval algorithms are used to ensure consistency across Meteosat satellites. The present study compares the performance of two single-channel LST retrieval algorithms: (1) A physical radiative transfer-based mono-window (PMW); and (2) a statistical mono-window model (SMW). The performance of the single-channel algorithms is assessed using a database of synthetic radiances for a wide range of atmospheric profiles and surface variables. The two single-channel algorithms are evaluated against the commonly-used generalized split-window (GSW) model. The three algorithms are verified against more than 60,000 LST ground observations with dry to very moist atmospheres (total column water vapor (TCWV) 1–56 mm). Except for very moist atmospheres (TCWV > 45 mm), results show that Meteosat single-channel retrievals match those of the GSW algorithm by 0.1–0.5 K. This study also outlines that it is possible to put realistic uncertainties on Meteosat single-channel LSTs, except for very moist atmospheres: simulated theoretical uncertainties are within 0.3–1.0 K of the in situ root mean square differences for TCWV < 45 mm.

Cloud properties and the Earth’s radiation budget are defined as essential climate variables by the Global Climate Observing System (GCOS). The cloud albedo is a measure for the portion of solar radiation reflected back to space by clouds. This information is essential for the analysis and interpretation of the Earth’s radiation budget and the solar surface irradiance. We present and discuss a method for the production of the effective cloud albedo and the solar surface irradiance based on the visible channel (0.45–1 μm) on-board of the Meteosat satellites. This method includes a newly developed self-calibration approach and has been used to generate a 23-year long (1983–2005) continuous and validated climate data record of the effective cloud albedo and the solar surface irradiance. Using this climate data record we demonstrate the ability of the method to generate the two essential climate variables in high accuracy and homogeneity. Further on, we discuss the role of the cloud albedo within climate monitoring and analysis. We found trends with opposite sign in the observed effective cloud albedo resulting in positive trends in the solar surface irradiance over ocean and partly negative trends over land. Ground measurements are scarce over the ocean and thus satellite-derived effective cloud albedo and solar surface irradiance constitutes a unique observational data source. Within this scope it has to be considered that the ocean is the main energy reservoir of the Earth, which emphasises the role of satellite-observed effective cloud albedo and derived solar surface irradiance as essential climate variables for climate monitoring and analysis.

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.