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Abstract Recent studies have used numerical models to estimate the collection efficiency of solid precipitation gauges when exposed to the wind in both shielded and unshielded configurations. The models used computational fluid dynamics (CFD) simulations of the airflow pattern generated by the aerodynamic response to the gauge–shield geometry. These are used as initial conditions to perform Lagrangian tracking of solid precipitation particles. Validation of the results against field observations yielded similarities in the overall behavior, but the model output only approximately reproduced the dependence of the experimental collection efficiency on wind speed. This paper presents an improved snowflake trajectory modeling scheme due to the inclusion of a dynamically determined drag coefficient. The drag coefficient was estimated using the local Reynolds number as derived from CFD simulations within a time-independent Reynolds-averaged Navier–Stokes approach. The proposed dynamic model greatly improves the consistency of results with the field observations recently obtained at the Marshall Field winter precipitation test bed in Boulder, Colorado.

It is increasingly being recognized that global ecological research requires novel methods and strategies in which to combine process‐based ecological models and data in cohesive, systematic ways. In process‐based model applications, inherent spatial and temporal heterogeneities found within terrestrial ecosystems may lead to the uncertainties of model predictions. To reduce simulation uncertainties due to inaccurate model parameters, the Markov Chain Monte Carlo (MCMC) method was applied in this study to improve the estimations of four key parameters used in the process‐based ecosystem model of TRIPLEX‐FLUX. These four key parameters include a maximum photosynthetic carboxylation rate of 25°C (Vmax), an electron transport (Jmax) light‐saturated rate within the photosynthetic carbon reduction cycle of leaves, a coefficient of stomatal conductance (m), and a reference respiration rate of 10°C (R10). Seven forest flux tower sites located across North America were used to investigate and facilitate understanding of the daily variation in model parameters for three deciduous forests, three evergreen temperate forests, and one evergreen boreal forest. Eddy covariance CO 2 exchange measurements were assimilated to optimize the parameters in the year 2006. After parameter optimization and adjustment took place, net ecosystem production prediction significantly improved (by approximately 25%) compared to the CO 2 flux measurements taken at the seven forest ecosystem sites. Results suggest that greater seasonal variability occurs in broadleaf forests in respect to the selected parameters than in needleleaf forests. This study also demonstrated that the model‐data fusion approach by incorporating MCMC method is able to better estimate parameters and improve simulation accuracy for different ecosystems located across North America.

It has been known for a long time that the shape of ice crystals depends on both the air temperature and the relative humidity of the environment. The relationships among these factors have been summarized in classification diagrams and are intensively referred to in the cloud physics literature. To put in perspective the atmospheric conditions in which the different ice crystal habits grow with respect to the level of saturation in the atmosphere, the vapor density excess and supersaturation with respect to ice at liquid water saturation have been included on those diagrams as a function of air temperature. Over the years, the definition of the water saturation included in those types of diagrams has been misdefined. The goal of this study is to show that an error has been introduced in the definition of the excess of water vapor with respect to ice.

Abstract The accurate measurement of snowfall is important in various fields of study such as climate variability, transportation, and water resources. A major concern is that snowfall measurements are difficult and can result in significant errors. For example, collection efficiency of most gauge–shield configurations generally decreases with increasing wind speed. In addition, much scatter is observed for a given wind speed, which is thought to be caused by the type of snowflake. Furthermore, the collection efficiency depends strongly on the reference used to correct the data, which is often the Double Fence Intercomparison Reference (DFIR) recommended by the World Meteorological Organization. The goal of this study is to assess the impact of weather conditions on the collection efficiency of the DFIR. Note that the DFIR is defined as a manual gauge placed in a double fence. In this study, however, only the double fence is being investigated while still being called DFIR. To address this issue, a detailed analysis of the flow field in the vicinity of the DFIR is conducted using computational fluid dynamics. Particle trajectories are obtained to compute the collection efficiency associated with different precipitation types for varying wind speed. The results show that the precipitation reaching the center of the DFIR can exceed 100% of the actual precipitation, and it depends on the snowflake type, wind speed, and direction. Overall, this study contributes to a better understanding of the sources of uncertainty associated with the use of the DFIR as a reference gauge to measure snowfall.

Abstract Understanding the responses of lake systems to past climate change and human activity is critical for assessing and predicting the fate of lake carbon (C) in the future. In this study, we synthesized records of the sediment accumulation from 82 lakes and of C sequestration from 58 lakes with direct organic C measurements throughout China. We also identified the controlling factors of the long‐term sediment and C accumulation dynamics in these lakes during the past 12 ka (1 ka = 1000 cal yr BP ). Our results indicated an overall increasing trend of sediment and C accumulation since 12 ka, with an accumulation peak in the last couple of millennia for lakes in China, corresponding to terrestrial organic matter input due to land‐use change. The Holocene lake sediment accumulation rate ( SAR ) and C accumulation rate ( CAR ) averaged (mean ± SE ) 0.47 ± 0.05 mm yr −1 and 7.7 ± 1.4 g C m −2  yr −1 in China, respectively, comparable to the previous estimates for boreal and temperate regions. The SAR for lakes in the East Plain of subtropical China (1.05 ± 0.28 mm yr −1 ) was higher than those in other regions ( P  <   0.05). However, CAR did not vary significantly among regions. Overall, the variability and history of climate and anthropogenic interference regulated the temporal and spatial dynamics of sediment and C sequestration for lakes in China. We estimated the total amount of C burial in lakes of China as 8.0 ± 1.0 Pg C. This first estimation of total C storage and dynamics in lakes of China confirms the importance of lakes in land C budget in monsoon‐influenced regions.

Dynamic global vegetation models (DGVMs) typically track the material and energy cycles in ecosystems with finite plant functional types (PFTs). Increasingly, the community ecology and modelling studies recognize that current PFT scheme is not sufficient for simulating ecological processes. Recent advances in the study of plant functional traits (FTs) in community ecology provide a novel and feasible approach for the improvement of PFT-based DGVMs. This paper reviews the development of current DGVMs over recent decades. After characterizing the advantages and disadvantages of the PFT-based scheme, it summarizes trait-based theories and discusses the possibility of incorporating FTs into DGVMs. More importantly, this paper summarizes three strategies for constructing next-generation DGVMs with FTs. Finally, the method’s limitations, current challenges and future research directions for FT theory are discussed for FT theory. We strongly recommend the inclusion of several FTs, namely specific leaf area (SLA), leaf nitrogen content (LNC), carbon isotope composition of leaves (Leaf δ 13 C), the ratio between leaf-internal and ambient mole fractions of CO 2 (Leaf C i /C a ), seed mass and plant height. These are identified as the most important in constructing DGVMs based on FTs, which are also recognized as important ecological strategies for plants. The integration of FTs into dynamic vegetation models is a critical step towards improving the results of DGVM simulations; communication and cooperation among ecologists and modellers is equally important for the development of the next generation of DGVMs.

Abstract Aim The fluctuations of atmospheric methane ( CH 4 ) that have occurred in recent decades are not fully understood, particularly with regard to the contribution from wetlands. The application of spatially explicit parameters has been suggested as an effective method for reducing uncertainties in bottom‐up approaches to wetland CH 4 emissions, but has not been included in recent studies. Our goal was to estimate spatio‐temporal patterns of global wetland CH 4 emissions using a process model and then to identify the contribution of wetland emissions to atmospheric CH 4 fluctuations. Location Global. Methods A process‐based model integrated with full descriptions of methanogenesis ( TRIPLEX‐GHG ) was used to simulate global wetland CH 4 emissions. Results Global annual wetland CH 4 emissions ranged from 209 to 245  T g CH 4 year −1 between 1901 and 2012, with peaks occurring in 1991 and 2012. There is a decreasing trend between 1990 and 2010 with a rate of approximately 0.48  T g CH 4 year −1 , which was largely caused by emissions from tropical wetlands showing a decreasing trend of 0.44  T g CH 4 year −1 since the 1970s. Emissions from tropical, temperate and high‐latitude wetlands comprised 59, 26 and 15% of global emissions, respectively. Main conclusion Global wetland CH 4 emissions, the interannual variability of which was primary controlled by tropical wetlands, partially drive the atmospheric CH 4 burden. The stable to decreasing trend in wetland CH 4 emissions, a result of a balance of emissions from tropical and extratropical wetlands, was a particular factor in slowing the atmospheric CH 4 growth rate during the 1990s. The rapid decrease in tropical wetland CH 4 emissions that began in 2000 was supposed to offset the increase in anthropogenic emissions and resulted in a relatively stable level of atmospheric CH 4 from 2000 to 2006. Increasing wetland CH 4 emissions, particularly after 2010, should be an important contributor to the growth in atmospheric CH 4 seen since 2007.