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Abstract. ICESat has provided surface elevation measurements of the ice sheets since the launch in January 2003, resulting in a unique dataset for monitoring the changes of the cryosphere. Here, we present a novel method for determining the mass balance of the Greenland ice sheet, derived from ICESat altimetry data. Three different methods for deriving elevation changes from the ICESat altimetry dataset are used. This multi-method approach provides a method to assess the complexity of deriving elevation changes from this dataset. The altimetry alone can not provide an estimate of the mass balance of the Greenland ice sheet. Firn dynamics and surface densities are important factors that contribute to the mass change derived from remote-sensing altimetry. The volume change derived from ICESat data is corrected for changes in firn compaction over the observation period, vertical bedrock movement and an intercampaign elevation bias in the ICESat data. Subsequently, the corrected volume change is converted into mass change by the application of a simple surface density model, in which some of the ice dynamics are accounted for. The firn compaction and density models are driven by the HIRHAM5 regional climate model, forced by the ERA-Interim re-analysis product, at the lateral boundaries. We find annual mass loss estimates of the Greenland ice sheet in the range of 191 ± 23 Gt yr−1 to 240 ± 28 Gt yr−1 for the period October 2003 to March 2008. These results are in good agreement with several other studies of the Greenland ice sheet mass balance, based on different remote-sensing techniques.
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Abstract The ability of four regional climate models (RCMs) to represent the Indian monsoon was verified in a consistent framework for the period 1981–2000 using the 45-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) as lateral boundary forcing data. During the monsoon period, the RCMs are able to capture the spatial distribution of precipitation with a maximum over the central and west coast of India, but with important biases at the regional scale on the east coast of India in Bangladesh and Myanmar. Most models are too warm in the north of India compared to the observations. This has an impact on the simulated mean sea level pressure from the RCMs, being in general too low compared to ERA-40. Those biases perturb the land–sea temperature and pressure contrasts that drive the monsoon dynamics and, as a consequence, lead to an overestimation of wind speed, especially over the sea. The timing of the monsoon onset of the RCMs is in good agreement with the one obtained from observationally based gridded datasets, while the monsoon withdrawal is less well simulated. A Hovmöller diagram representation of the mean annual cycle of precipitation reveals that the meridional motion of the precipitation simulated by the RCMs is comparable to the one observed, but the precipitation amounts and the regional distribution differ substantially between the four RCMs. In summary, the spread at the regional scale between the RCMs indicates that important feedbacks and processes are poorly, or not, taken into account in the state-of-the-art regional climate models.
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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.