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Abstract Applying allometric equations in combination with forest inventory data is an effective approach to use when qualifying forest biomass and carbon storage on a regional scale. The objectives of this study were to (1) develop general allometric tree component biomass equations and (2) investigate tree biomass allocation patterns for Pinus massoniana , a principal tree species native to southern China, by applying 197 samples across 20 site locations. The additive allometric equations utilized to compute stem, branch, needle, root, aboveground, and total tree biomass were developed by nonlinear seemingly unrelated regression. Results show that the relative proportion of stem biomass to tree biomass increased while the contribution of canopy biomass to tree biomass decreased as trees continued to grow through time. Total root biomass was a large biomass pool in itself, and its relative proportion to tree biomass exhibited a slight increase with tree growth. Although equations employing stem diameter at breast height (dbh) alone as a predictor could accurately predict stem, aboveground, root, and total tree biomass, they were poorly fitted to predict the canopy biomass component. The inclusion of the tree height ( H ) variable either slightly improved or did not in any way increase model fitness. Validation results demonstrate that these equations are suitable to estimate stem, aboveground, and total tree biomass across a broad range of P . massoniana stands on a regional scale.

The hypothesis according to which higher sulphate concentrations favor ice clouds made of larger ice crystals is tested using data sets from the CloudSat and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellites. This is a potential consequence of the sulphate‐induced freezing inhibition (SIFI) effect, namely, the hypothesis that sulphates contribute to inhibit the onset of ice crystal formation by deactivating ice‐forming nuclei during Arctic winter. A simple index based on the backscattering at 532 nm and the color ratio from the CALIPSO lidar measurements is compared against in situ sulphate concentration time series and used as a proxy for this variable. An algorithm using the lidar data and the CloudSat radar microphysical retrievals is also developed for identifying cloud types, focusing on those supposedly favored by the SIFI effect. The analysis includes the effect of the lidar off‐nadir angle on the sulphate index and the cloud classification, the validation of the index, as well as the production of circum‐Arctic maps of the sulphate index and of the SIFI‐favored clouds fraction. The increase of the lidar off‐nadir angle is shown to cause an increase in the measured depolarization ratio and hence in the ability to detect ice crystals. The index correlates positively with both sulphates and sea salt concentrations, with a Pearson correlation coefficient ( ) varying from 0.10 to 0.42 for the different comparisons performed. Ultimate findings are the results of two correlation tests of the SIFI effect, which allow for a new outlook on its possible role in the Arctic troposphere during winter.

Abstract The phase of precipitation formed within the atmosphere is highly dependent on the vertical temperature profile through which it falls. In particular, several precipitation types can form in an environment with a melting layer aloft and a refreezing layer below. These precipitation types include freezing rain, ice pellets, wet snow, and slush. To examine the formation of such precipitation, a bulk microphysics scheme was used to compare the characteristics of the hydrometeors produced by the model and observed by a research aircraft flight during the 1998 ice storm near Montreal, Canada. The model reproduced several of the observed key precipitation characteristics. Sensitivity tests on the precipitation types formed during the ice storm were also performed. These tests utilized temperature profiles produced by the North American Regional Reanalysis. The results show that small variations (±0.5°C) in the temperature profiles as well as in the precipitation rate can have major impacts on the types of precipitation formed at the surface. These results impose strong requirements on the accuracy needed by prediction models.

Abstract Several types of precipitation, such as freezing rain, ice pellets, and wet snow, are commonly observed during winter storms. The objective of this study is to better understand the formation of these winter precipitation types. To address this issue, detailed melting and refreezing of precipitation was added onto an existing bulk microphysics scheme. These modifications allow the formation of mixed-phase particles and these particles in turn lead to, or affect, the formation of many of the other types of precipitation. The precipitation type characteristics, such as the mass content, liquid fraction, and threshold diameters formed during a storm over St John’s, Newfoundland, Canada, are studied and compared with observations. Many of these features were reproduced by the model. Sensitivity experiments with the model were carried out to examine the dependence of precipitation characteristics in this event on thresholds of particle evolution in the new parameterization.

Abstract. The early to mid-Holocene thermal optimum is a well-known feature in a wide variety of paleoclimate archives from the Northern Hemisphere. Reconstructed summer temperature anomalies from across northern Europe show a clear maximum around 6000 years before present (6 ka). For the marine realm, Holocene trends in sea-surface temperature reconstructions for the North Atlantic and Norwegian Sea do not exhibit a consistent pattern of early to mid-Holocene warmth. Sea-surface temperature records based on alkenones and diatoms generally show the existence of a warm early to mid-Holocene optimum. In contrast, several foraminifer and radiolarian based temperature records from the North Atlantic and Norwegian Sea show a cool mid-Holocene anomaly and a trend towards warmer temperatures in the late Holocene. In this paper, we revisit the foraminifer record from the Vøring Plateau in the Norwegian Sea. We also compare this record with published foraminifer based temperature reconstructions from the North Atlantic and with modelled (CCSM3) upper ocean temperatures. Model results indicate that while the seasonal summer warming of the sea-surface was stronger during the mid-Holocene, sub-surface depths experienced a cooling. This hydrographic setting can explain the discrepancies between the Holocene trends exhibited by phytoplankton and zooplankton based temperature proxy records.

Abstract The Integrated Biosphere Simulator is used to evaluate the spatial and temporal patterns of the crucial hydrological variables [run‐off and actual evapotranspiration (AET)] of the water balance across China for the period 1951–2006 including a precipitation analysis. Results suggest three major findings. First, simulated run‐off captured 85% of the spatial variability and 80% of the temporal variability for 85 hydrological gauges across China. The mean relative errors were within 20% for 66% of the studied stations and within 30% for 86% of the stations. The Nash–Sutcliffe coefficients indicated that the quantity pattern of run‐off was also captured acceptably except for some watersheds in southwestern and northwestern China. The possible reasons for underestimation of run‐off in the Tibetan plateau include underestimation of precipitation and uncertainties in other meteorological data due to complex topography, and simplified representations of the soil depth attribute and snow processes in the model. Second, simulated AET matched reasonably with estimated values calculated as the residual of precipitation and run‐off for watersheds controlled by the hydrological gauges. Finally, trend analysis based on the Mann–Kendall method indicated that significant increasing and decreasing patterns in precipitation appeared in the northwest part of China and the Yellow River region, respectively. Significant increasing and decreasing trends in AET were detected in the Southwest region and the Yangtze River region, respectively. In addition, the Southwest region, northern China (including the Heilongjiang, Liaohe, and Haihe Basins), and the Yellow River Basin showed significant decreasing trends in run‐off, and the Zhemin hydrological region showed a significant increasing trend. Copyright © 2009 John Wiley & Sons, Ltd.

There is a lack in representation of biosphere–atmosphere interactions in current climate models. To fill this gap, one may introduce vegetation dynamics in surface transfer schemes or couple global climate models (GCMs) with vegetation dynamics models. As these vegetation dynamics models were not designed to be included in GCMs, how are the latest generation dynamic global vegetation models (DGVMs) suitable for use in global climate studies? This paper reviews the latest developments in DGVM modelling as well as the development of DGVM–GCM coupling in the framework of global climate studies. Limitations of DGVM and coupling are shown and the challenges of these methods are highlighted. During the last decade, DGVMs underwent major changes in the representation of physical and biogeochemical mechanisms such as photosynthesis and respiration processes as well as in the representation of regional properties of vegetation. However, several limitations such as carbon and nitrogen cycles, competition, land-use and land-use changes, and disturbances have been identified. In addition, recent advances in model coupling techniques allow the simulation of the vegetation–atmosphere interactions in GCMs with the help of DGVMs. Though DGVMs represent a good alternative to investigate vegetation–atmosphere interactions at a large scale, some weaknesses in evaluation methodology and model design need to be further investigated to improve the results.

Our current understanding of terrestrial carbon processes is represented in various models used to integrate and scale measurements of CO 2 exchange from remote sensing and other spatiotemporal data. Yet assessments are rarely conducted to determine how well models simulate carbon processes across vegetation types and environmental conditions. Using standardized data from the North American Carbon Program we compare observed and simulated monthly CO 2 exchange from 44 eddy covariance flux towers in North America and 22 terrestrial biosphere models. The analysis period spans ∼220 site‐years, 10 biomes, and includes two large‐scale drought events, providing a natural experiment to evaluate model skill as a function of drought and seasonality. We evaluate models' ability to simulate the seasonal cycle of CO 2 exchange using multiple model skill metrics and analyze links between model characteristics, site history, and model skill. Overall model performance was poor; the difference between observations and simulations was ∼10 times observational uncertainty, with forested ecosystems better predicted than nonforested. Model‐data agreement was highest in summer and in temperate evergreen forests. In contrast, model performance declined in spring and fall, especially in ecosystems with large deciduous components, and in dry periods during the growing season. Models used across multiple biomes and sites, the mean model ensemble, and a model using assimilated parameter values showed high consistency with observations. Models with the highest skill across all biomes all used prescribed canopy phenology, calculated NEE as the difference between GPP and ecosystem respiration, and did not use a daily time step.