• Energy Research

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.

The productive cellulosic crops switchgrass and Miscanthus are considered as viable biofuel sources. To meet the 2022 national biofuel target mandate, actions must be taken, e.g., maize cultivation must be intensified and expanded, and other biofuel crops (switchgrass and Miscanthus ) must be cultivated. This raises questions on the use efficiencies of land and water; to date, the demand on these resources to meet the national biofuel target has rarely been analyzed. Here, we present a data-model assimilation analysis, assuming that maize, switchgrass and Miscanthus will be grown on currently available croplands in the US. Model simulations suggest that maize can produce 3.0–5.4 kiloliters (kl) of ethanol for every hectare of land, depending on the feedstock to ethanol conversion efficiency; Miscanthus has more than twice the biofuel production capacity relative to maize, and switchgrass is the least productive of the three potential sources of ethanol. To meet the biofuel target, about 26.5 million hectares of land and over 90 km ^3 of water (of evapotranspiration) are needed if maize grain alone is used. If Miscanthus was substituted for maize, the process would save half of the land and one third of the water. With more advanced biofuel conversion technology for Miscanthus , only nine million hectares of land and 45 km ^3 of water would probably meet the national target. Miscanthus could be a good alternative biofuel crop to maize due to its significantly lower demand for land and water on a per unit of ethanol basis.

AbstractPiao et al. (2022) presents an interesting study on vegetation phenology from a novel angle. The study examined the speed of canopy development and senescence, or the “acceleration of vegetation phenological changes”, and investigated its relationship with the trend of vegetation greening/browning and the underlying climate driving factors. It is an important contribution to the understanding of vegetation response to climate change, and may inspire new directions of vegetation phenology research.

The projected rise in temperature in the 21st century will alter forest ecosystem functioning and carbon dynamics. To date, the acclimation of plant photosynthesis to rising temperature has not been adequately considered in earth system models. Here we present a study on regional ecosystem carbon dynamics under future climate scenarios incorporating temperature acclimation effects into a large-scale ecosystem model, the terrestrial ecosystem model (TEM). We first incorporate a general formulation of the temperature acclimation of plant photosynthesis into TEM, and then apply the revised model to the forest ecosystems of the conterminous United States for the 21st century under the future Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) climate scenarios A1FI, A2, B1 and B2. We find that there are significant differences between the estimates of carbon dynamics from the previous and the revised models. The largest differences occur under the A1FI scenario, in which the model that considers acclimation effects predicts that the region will act as a carbon sink, and that cumulative carbon in the 21st century will be 35 Pg C higher than the estimates from the model that does not consider acclimation effects. Our results further indicate that in the region there are spatially different responses to temperature acclimation effects. This study suggests that terrestrial ecosystem models should take temperature acclimation effects into account so as to more accurately quantify ecosystem carbon dynamics at regional scales.Keywords: climate change, temperature acclimation, forest ecosystem, photosynthesis, carbon dynamics, TEM(Published: 14 January 2013)Citation: Tellus B 2013, 65, 19156, http://dx.doi.org/10.3402/tellusb.v65i0.19156

AbstractGrowing concerns about energy and the environment have led to worldwide use of bioenergy. Switching from food crops to biofuel crops is an option to meet the fast‐growing need for biofuel feedstocks. This land use change consequently affects the ecosystem carbon balance. In this study, we used a biogeochemistry model, the Terrestrial Ecosystem Model, to evaluate the impacts of this change on the carbon balance, bioenergy production, and agricultural yield, assuming that several land use change scenarios from corn, soybean, and wheat to biofuel crops of switchgrass and Miscanthus will occur. We found that biofuel crops have much higher net primary production (NPP) than soybean and wheat crops. When food crops from current agricultural lands were changed to different biofuel crops, the national total NPP increased in all cases by a range of 0.14–0.88 Pg C yr−1, except while switching from corn to switchgrass when a decrease of 14% was observed. Miscanthus is more productive than switchgrass, producing about 2.5 times the NPP of switchgrass. The net carbon loss ranges from 1.0 to 6.3 Tg C yr−1 if food crops are changed to switchgrass, and from 0.4 to 6.7 Tg C yr−1 if changed to Miscanthus. The largest loss was observed when soybean crops were replaced with biofuel crops. Soil organic carbon increased significantly when land use changed, reaching 100 Mg C ha−1 in biofuel crop ecosystems. When switching from food crops to Miscanthus, the per unit area croplands produced a larger amount of ethanol than that of original food crops. In comparison, the land use change from wheat to Miscanthus produced more biomass and sequestrated more carbon. Our study suggests that Miscanthus could better serve as an energy crop than food crops or switchgrass, considering both economic and environmental benefits.

AbstractA spring phenology model that combines photoperiod with accumulated heating and chilling to predict spring leaf‐out dates is optimized using PhenoCam observations and coupled into the Community Land Model (CLM) 4.5. In head‐to‐head comparison (using satellite data from 2003 to 2013 for validation) for model grid cells over the Northern Hemisphere deciduous broadleaf forests (5.5 million km2), we found that the revised model substantially outperformed the standard CLM seasonal‐deciduous spring phenology submodel at both coarse (0.9 × 1.25°) and fine (1 km) scales. The revised model also does a better job of representing recent (decadal) phenological trends observed globally by MODIS, as well as long‐term trends (1950–2014) in the PEP725 European phenology dataset. Moreover, forward model runs suggested a stronger advancement (up to 11 days) of spring leaf‐out by the end of the 21st century for the revised model. Trends toward earlier advancement are predicted for deciduous forests across the whole Northern Hemisphere boreal and temperate deciduous forest region for the revised model, whereas the standard model predicts earlier leaf‐out in colder regions, but later leaf‐out in warmer regions, and no trend globally. The earlier spring leaf‐out predicted by the revised model resulted in enhanced gross primary production (up to 0.6 Pg C yr−1) and evapotranspiration (up to 24 mm yr−1) when results were integrated across the study region. These results suggest that the standard seasonal‐deciduous submodel in CLM should be reconsidered, otherwise substantial errors in predictions of key land–atmosphere interactions and feedbacks may result.

Ongoing climate variability and change is impacting pollen exposure dynamics among sensitive populations. However, pollen data that can provide beneficial information to allergy experts and patients alike remains elusive. The lack of high spatial resolution pollen data has resulted in a growing interest in using phenology information that is derived using satellite observations to infer key pollen events including start of pollen season (SPS), timing of peak pollen season (PPS), and length of pollen season (LPS). However, it remains unclear if the agreement between satellite-based phenology information (e.g. start of season: SOS) and the in-situ pollen dynamics vary based on the type of satellite product itself or the processing methods used. To address this, we investigated the relationship between vegetation phenology indicator (SOS) derived from two separate sensor/satellite observations (MODIS, Landsat), and two different processing methods (double logistic regression (DLM) vs hybrid piecewise logistic regression (HPLM)) with in-situ pollen season dynamics (SPS, PPS, LPS) for three dominant allergenic tree pollen species (birch, oak, and poplar) that dominate the springtime allergy season in North America. Our results showed that irrespective of the data processing method (i.e. DLM vs HPLM), the MODIS-based SOS to be more closely aligned with the in-situ SPS, and PPS while upscaled Landsat based SOS had a better precision. The data products obtained using DLM processing methods tended to perform better than the HPLM based methods. We further showed that MODIS based phenology information along with temperature and latitude can be used to infer in-situ pollen dynamic for tree pollen during spring time. Our findings suggest that satellite-based phenology information may be useful in the development of early warning systems for allergic diseases.

Abstract Heterotrophic respiration (Rh), microbial processing of soil organic matter to carbon dioxide (CO 2 ), is a major, yet highly uncertain, carbon (C) flux from terrestrial systems to the atmosphere. Temperature sensitivity of Rh is often represented with a simple Q 10 function in ecosystem models and earth system models (ESMs), sometimes accompanied by an empirical soil moisture modifier. More explicit representation of the effects of soil moisture, substrate supply, and their interactions with temperature has been proposed as a way to disentangle the confounding factors of apparent temperature sensitivity of Rh and improve the performance of ecosystem models and ESMs. The objective of this work was to insert into an ecosystem model a more mechanistic, but still parsimonious, model of environmental factors controlling Rh and evaluate the model performance in terms of soil and ecosystem respiration. The Dual Arrhenius and Michaelis-Menten (DAMM) model simulates Rh using Michaelis-Menten, Arrhenius, and diffusion functions. Soil moisture affects Rh and its apparent temperature sensitivity in DAMM by regulating the diffusion of oxygen, soluble C substrates, and extracellular enzymes to the enzymatic reaction site. Here, we merged the DAMM soil flux model with a parsimonious ecosystem flux model, FoBAAR (Forest Biomass, Assimilation, Allocation and Respiration). We used high-frequency soil flux data from automated soil chambers and landscape-scale ecosystem fluxes from eddy covariance towers at two AmeriFlux sites (Harvard Forest, MA and Howland Forest, ME) in the northeastern USA to estimate parameters, validate the merged model, and to quantify the uncertainties in a multiple constraints approach. The optimized DAMM-FoBAAR model better captured the seasonal and inter-annual dynamics of soil respiration (Soil R) compared to the FoBAAR-only model for the Harvard Forest, where higher frequency and duration of drying events significantly regulate substrate supply to heterotrophs. However, DAMM-FoBAAR showed improvement over FoBAAR-only at the boreal transition Howland Forest only in unusually dry years. The frequency of synoptic-scale dry periods is lower at Howland, resulting in only brief water limitation of Rh in some years. At both sites, the declining trend of soil R during drying events was captured by the DAMM-FoBAAR model; however, model performance was also contingent on site conditions, climate, and the temporal scale of interest. While the DAMM functions require a few more parameters than a simple Q 10 function, we have demonstrated that they can be included in an ecosystem model and reduce the model-data mismatch. Moreover, the mechanistic structure of the soil moisture effects using DAMM functions should be more generalizable than the wide variety of empirical functions that are commonly used, and these DAMM functions could be readily incorporated into other ecosystem models and ESMs.