Winter sea ice production in the Arctic coastal polynyas was compared between a satellite microwave-based estimate and a pan-Arctic sea ice-ocean model. The Alaskan coast, Novaya Zemlya, and North Water were chosen as target regions in this study. The interannual model experiment showed high correlation of sea ice production with the SSM/I-based variability from 1993 to 2013 in the Alaskan coast and Novaya Zemlya regions. On the other hand, the modeled production was frequently smaller than the satellite values. The modeling analyses suggested that offshore-ward sea ice transport and upward ocean heat transport were intensified in the same years, in the Alaskan coast and North Water regions. The wind stress with specific direction caused both the processes. Since the satellite algorithm used in this study did not include the ocean heat flux under sea ice, on which the wind-driven upwelling and turbulent mixing work, the improvement using open water mask was expected. For surface heat budget calculation, the model and satellite algorithm utilized different atmospheric datasets. Whereas the air temperature obtained from two major reanalysis data showed the close values in the Alaskan coast and Novaya Zemlya regions, the obvious temperature bias would be one of factors for an error in sea ice production in the North Water region. For further model improvements, the formulation of sea ice internal stress should be verified to more precisely represent polynya expanding processes. To update satellite algorithm, snow and fine-scale open lead would be key issues.
In this study, we demonstrate a method for quantifying the solar shading effect of urban trees. Leaf area density (LAD) distribution is estimated using multi-return airborne light detection and ranging (LiDAR); then, the estimated distribution is applied to a radiative transfer model of vegetation to calculate the direct photosynthetically active radiation (PAR) transmittance. When first and single returns are used to estimate the LAD distribution, which is the same as the previously developed methods by other researchers, LAD is estimated with a large error in the lower part of the crown. The estimation error is an obstacle for an accurate calculation of direct PAR transmittance. Therefore, a method using the last and intermediate returns in addition to the first and single returns was examined. We verified the estimation accuracy of the LAD distribution using the terrestrial LiDAR data of a single Japanese zelkova (Zelkova serrata) tree. We confirmed that using the last and intermediate returns improves the estimation accuracy of the entire crown area, especially in the lower part of the crown. Improvement of the spatial resolution of the external crown geometry and correction of the LAD for the voxels where there are no airborne LiDAR returns from the leaves were also conducted. Subsequently, the estimated LAD distribution was applied to a radiative transfer model, and then, the direct PAR transmittance was calculated. The PAR under the Z.serrata tree was measured using a device with a 1-m probe in which PAR sensors are embedded. We obtained PAR distribution by moving the device in the direction perpendicular to the probe, and the calculated transmittance was then compared with the measured one. The comparison showed that when the estimated LAD distribution with the LAD correction is used, direct PAR transmittance is accurately calculated, regardless of solar altitude.
The global carbon cycle has feedbacks on the global climate, and climate-carbon cycle interactions are explicitly represented in earth system models (ESMs). In ESMs, leaf area index (LAI) is a key variable for projecting future environmental changes, but it might be one of the most difficult to precisely predict. In this research, historical LAI changes reproduced in the ESM named “Model for Interdisciplinary Research on Climate ESM (MIROC-ESM)” are analyzed, focusing on impacts of CO2 fertilization effects, climate change, and land-use change. The model showed reasonable seasonality of LAI in global scale although the absolute value of this index was larger than the observation. The CO2 fertilization effect increases global LAI, and this was found to be nearly ubiquitous. However, an LAI increase is partially offset by the influence of historical climate change induced by anthropogenic greenhouse gas emissions. In addition to these environmental change impacts, human perturbations of the carbon cycle through land-use change dramatically altered global LAI. Although land-use change may have both positive and negative impacts on LAI, most regions showed negative impacts in the historical simulation, and land-use change had greater impacts than environmental change. Further study is recommended, such as long-term observation, modeling, and simulations of historical LAI change, toward improving modeling of future climate change.
The Greenhouse gases Observing SATellite (GOSAT) was launched on January 2009 to measure spatiotemporal variability in column carbon dioxide (CO2) and methane (CH4) concentrations over the globe. This paper aims to give an overview of GOSAT observations and applications of the data to inverse modeling of distributions of the atmospheric tracer gases.