An Efficient Method of Estimating Downward Solar Radiation Based on the MODIS Observations for the Use of Land Surface Modeling

Solar radiation is a critical variable in global change sciences. While most of the current global datasets provide only the total downward solar radiation, we aim to develop a method to estimate the downward global land surface solar radiation and its partitioned direct and diffuse components, which provide the necessary key meteorological inputs for most land surface models. We developed a simple satellite-based computing scheme to enable fast and reliable estimation of these variables. The global Moderate Resolution Imaging Spectroradiometer (MODIS) products at 1° spatial resolution for the period 2003–2011 were used as the forcing data. Evaluations at Baseline Surface Radiation Network (BSRN) sites show good agreement between the estimated radiation and ground-based observations. At all the 48 BSRN sites, the RMSE between the observations and estimations are 34.59, 41.98 and 28.06 W∙m−2 for total, direct and diffuse solar radiation, respectively. Our estimations tend to slightly overestimate the total and diffuse but underestimate the direct solar radiation. The errors may be related to the simple model structure and error of the input data. Our estimation is also comparable to the Clouds and Earth’s Radiant Energy System (CERES) data while shows notable improvement over the widely used National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) Reanalysis data. Using our MODIS-based datasets of total solar radiation and its partitioned components to drive land surface models should improve simulations of global dynamics of water, carbon and climate

Do we (need to) care about canopy radiation schemes in DGVMs? Caveats and potential impacts

Dynamic global vegetation models (DGVMs) are an essential part of current state-of-the-art Earth system models. In recent years, the complexity of DGVMs has increased by incorporating new important processes like, e.g., nutrient cycling and land cover dynamics, while biogeophysical processes like surface radiation have not been developed much further. Canopy radiation models are however very important for the estimation of absorption and reflected fluxes and are essential for a proper estimation of surface carbon, energy and water fluxes. The present study provides an overview of current implementations of canopy radiation schemes in a couple of state-of-the-art DGVMs and assesses their accuracy in simulating canopy absorption and reflection for a variety of different surface conditions. Systematic deviations in surface albedo and fractions of absorbed photosynthetic active radiation (faPAR) are identified and potential impacts are assessed. The results show clear deviations for both, absorbed and reflected, surface solar radiation fluxes. FaPAR is typically underestimated, which results in an underestimation of gross primary productivity (GPP) for the investigated cases. The deviation can be as large as 25% in extreme cases. Deviations in surface albedo range between −0.15 ≤ Δα ≤ 0.36, with a slight positive bias on the order of Δα ≈ 0.04. Potential radiative forcing caused by albedo deviations is estimated at −1.25 ≤ RF ≤ −0.8 (W m−2), caused by neglect of the diurnal cycle of surface albedo. The present study is the first one that provides an assessment of canopy RT schemes in different currently used DGVMs together with an assessment of the potential impact of the identified deviations. The paper illustrates that there is a general need to improve the canopy radiation schemes in DGVMs and provides different perspectives for their improvement

Upscaling fluxes from towers to regions, continents and global scales using datadriven approaches

Quantifying the current carbon cycle of terrestrial ecosystems requires that we translate spatially sparse measurements into consistent, gridded flux estimates at the regional scale. This is particularly challenging in heterogeneous regions such as the northern forests of the United States. We use a network of 17 eddy covariance flux towers deployed across the Upper Midwest region of northern Wisconsin and Michigan and upscale flux observations from towers to the regional scale. This region is densely instrumented and provides a unique test bed for regional upscaling. We develop a simple Diagnostic Carbon Flux Model (DCFM) and use flux observations and a data assimilation approach to estimate the model parameters. We then use the optimized model to produce gridded flux estimates across the region. We find that model parameters vary not only across plant functional types (PFT) but also within a given PFT. Our results show that the parameter estimates from a single site are not representative of the parameter values of a given PFT; cross-site (or joint) optimization using observations from multiple sites encompassing a range of site and climate conditions considerably improves the representativeness and robustness of parameter estimates. Parameter variability within a PFT can result in substantial variability in regional flux estimates. We also find that land cover representation including land cover heterogeneity and the spatial resolution and accuracy of land cover maps can lead to considerable uncertainty in regional flux estimates. In heterogeneous, complex regions, detailed and accurate land cover maps are essential for accurate estimation of regional fluxes

Upscaling carbon fluxes from towers to the regional scale: Influence of parameter variability and land cover representation on regional flux estimates

Quantifying the current carbon cycle of terrestrial ecosystems requires that we translate spatially sparse measurements into consistent, gridded flux estimates at the regional scale. This is particularly challenging in heterogeneous regions such as the northern forests of the United States. We use a network of 17 eddy covariance flux towers deployed across the Upper Midwest region of northern Wisconsin and Michigan and upscale flux observations from towers to the regional scale. This region is densely instrumented and provides a unique test bed for regional upscaling. We develop a simple Diagnostic Carbon Flux Model (DCFM) and use flux observations and a data assimilation approach to estimate the model parameters. We then use the optimized model to produce gridded flux estimates across the region. We find that model parameters vary not only across plant functional types (PFT) but also within a given PFT. Our results show that the parameter estimates from a single site are not representative of the parameter values of a given PFT; cross-site (or joint) optimization using observations from multiple sites encompassing a range of site and climate conditions considerably improves the representativeness and robustness of parameter estimates. Parameter variability within a PFT can result in substantial variability in regional flux estimates. We also find that land cover representation including land cover heterogeneity and the spatial resolution and accuracy of land cover maps can lead to considerable uncertainty in regional flux estimates. In heterogeneous, complex regions, detailed and accurate land cover maps are essential for accurate estimation of regional fluxes

Evaluation of Arctic land snow cover characteristics, surface albedo and temperature during the transition seasons from regional climate model simulations and satellite data

This paper evaluates the simulated Arctic land snow cover duration, snow water equivalent, snow cover fraction, surface albedo and land surface temperature in the regional climate model HIRHAM5 during 2008-2010, compared with various satellite and reanalysis data and one further regional climate model (COSMO-CLM). HIRHAM5 shows a general agreement in the spatial patterns and annual course of these variables, although distinct biases for specific regions and months are obvious. The most prominent biases occur for east Siberian deciduous forest albedo, which is overestimated in the simulation for snow covered conditions in spring. This may be caused by the simplified albedo parameterization (e.g. non-consideration of different forest types and neglecting the effect of fallen leaves and branches on snow for deciduous tree forest). The land surface temperature biases mirror the albedo biases in their spatial and temporal structures. The snow cover fraction and albedo biases can explain the simulated land surface temperature bias of ca. -3 °C over the Siberian forest area in spring

Effects of large solar zenith angles and cloud cover on underwater irradiance

Le processus de la photosynthèse nécessite l'énergie de la lumière solaire et, dans l’océan, se déroule essentiellement dans la couche euphotique. Outre les autres variables (à savoir la chlorophylle a et les paramètres photosynthétiques), une connaissance appropriée du champ lumineux en termes de rayonnement incident disponible sur la photosynthèse (PAR) à un emplacement, une profondeur et une heure et une date donnés, est requise par les modèles d'écosystème marin. Le travail inclus dans cette thèse examine comment des angles de zénith solaires plus grands et différentes conditions nuageuses caractéristiques des régions de haute latitude, en particulier dans l'Arctique, peuvent affecter la précision des estimations de l'éclairement de surface et dans la colonne d’eau. L’accent est également mis sur les variations du champs lumineux à haute fréquence liées à la nébulosité sur les estimations de la productivité primaire. Les PAR de surface estimés à partir de différents modèles ont été comparés à des mesures en série chronologique in situ à haute fréquence de données de PAR d'une bouée située en mer Méditerranée. Nous avons examiné comment les incertitudes dues aux angles de zénith solaires plus grands, en conditions nuageuses variables, pouvaient affecter la précision des estimations de l'éclairement de surface. La méthode de classement objectif a été utilisée pour identifier les meilleures méthodes. Le produit PAR de la NASA-Ocean Biology Processing Group (OBPG) a montré les meilleures performances globales, tandis que les PAR basées sur la méthode de la table de conversion (LUT) ont présenté les meilleures performances en termes de différence carrée moyenne, de biais sous ciel clair et également par temps couvert. D'autres méthodes basées sur des formulations empiriques ont montré la troisième meilleure performance par temps clair, tandis que par temps nuageux, elles présentaient de plus grandes incertitudes. Trois méthodes testées par faible ensoleillement ont montré des incertitudes allant jusqu'à 50% dans toutes les conditions du ciel. Les performances du modèle dépendent des propriétés et des produits de nuage...The process of photosynthesis requires the energy from sunlight and takes place essentially in the euphotic layer of the oceans. In addition to other variables (i.e., chlorophyll a and photosynthetic parameters) a suitable knowledge of light field in terms of photosynthetically available radiation (PAR) at any given location, depth and time is an important input parameter required by marine ecosystem models. The work included in this thesis examines how larger solar zenith angles, different cloud conditions that are characteristic features of high latitude regions, especially in Arctic, might affect the accuracy of surface irradiance estimates. Further, main focus was on the effects of high frequency variations in the light field on primary production. Surface PAR estimated from different models were compared with high frequency in situ time series measurements of PAR a buoy located in Mediterranean Sea. It was examined how uncertainties due to larger solar zenith angles under varying cloud conditions might affect the accuracy of surface irradiance. Objective ranking method was used to identify the best methods. Methods tested under low sun elevations exhibited uncertainties as large as 50% under all sky conditions. Model performances were dependent on cloud properties and products. Accuracy of a semianalytical model for coefficient of vertical diffuse attenuation of surface irradiance (kd!) based on optical properties inherent to the water itself (absorption and scattering), and solar zenith angle was examined under larger solar zenith angels and cloud conditions. Extensive radiative transfer simulations were performed to quantify the uncertainties due to large solar zenith angles and clouds on the estimates of diffuse attenuation coefficient. The uncertainties under both these conditions are due to the variability in the proportions of direct and diffuse parts of the total irradiance reaching the surface and in the water column. Also, an improved model parameterization proposed to estimate !"# under large solar zenith angels and cloud conditions was evaluated with Arctic in situ data exhibited good performances..

Characterizing Ice Cloud Particle Shape and Surface Roughness from Polarimetric Satellite Observations

The single scattering properties of ice cloud particles are inferred from spaceborne multi-angle satellite sensors with two newly developed noise-resilient retrieval techniques. The first presented method parameterizes the phase function and phase matrix elements by a few parameters to implement the maximum likelihood estimation in the retrieval system. The second method retrieves the renormalized phase function as a difference from a known phase function. The effect of noise is more predictable for both methods than the conventional “best-fit” method, which selects the best-fitting shape and surface roughness from a predetermined particle set. The first method is applied to the data from the Polarization and Directionality of the Earth’s Reflectance (POLDER) sensor. The retrieval results indicate that long column shape (ratio of basal face diameter to prism height greater than 9) with surface roughness parameter between 0.1 and 0.5 represents the extratropical observations well. Weak temperature dependence of the surface roughness is found in the extratropical data stratified by the cloud top temperature. The tropical retrieval was not successful, and the second method is applied to the Multi-angle Imaging Spectroradiometer (MISR) data. Short hexagonal column particles or their aggregates are found to match with estimated renormalized phase function. In addition to these results, the surface roughness simulation is summarized and the derivation of the δ-fit truncation technique for polarimetric radiative transfer is included

Characterizing Ice Cloud Particle Shape and Surface Roughness from Polarimetric Satellite Observations

The single scattering properties of ice cloud particles are inferred from spaceborne multi-angle satellite sensors with two newly developed noise-resilient retrieval techniques. The first presented method parameterizes the phase function and phase matrix elements by a few parameters to implement the maximum likelihood estimation in the retrieval system. The second method retrieves the renormalized phase function as a difference from a known phase function. The effect of noise is more predictable for both methods than the conventional “best-fit” method, which selects the best-fitting shape and surface roughness from a predetermined particle set. The first method is applied to the data from the Polarization and Directionality of the Earth’s Reflectance (POLDER) sensor. The retrieval results indicate that long column shape (ratio of basal face diameter to prism height greater than 9) with surface roughness parameter between 0.1 and 0.5 represents the extratropical observations well. Weak temperature dependence of the surface roughness is found in the extratropical data stratified by the cloud top temperature. The tropical retrieval was not successful, and the second method is applied to the Multi-angle Imaging Spectroradiometer (MISR) data. Short hexagonal column particles or their aggregates are found to match with estimated renormalized phase function. In addition to these results, the surface roughness simulation is summarized and the derivation of the δ-fit truncation technique for polarimetric radiative transfer is included