Shusen Wang

Generating historical AVHRR 1 km baseline satellite data records over Canada suitable for climate change studies

Generating historical AVHRR 1 km baseline satellite data records over Canada suitable for climate change studies Rasim Latifovic, Alexander P. Trishchenko, Ji Chen, William B. Park, Konstantin V. Khlopenkov, Richard Fernandes, Darren Pouliot, Calin Ungureanu, Yi Luo, Shusen Wang, Andrew Davidson, and Josef Cihlar Pages 324-346 Abstract. Satellite data are an important component of the global climate observing system (GCOS). To serve the purpose of climate change monitoring, these data should satisfy certain criteria in terms of the length of observations and the continuity and consistency between different missions and instruments. Despite the great potential and obvious advantages of satellite observations, such as frequent repeat cycles and global coverage, their use in climate studies is hindered by substantial difficulties arising from large data volumes, complicated processing, and significant computer resources required for archiving and analysis. Successful examples of satellite earth observation (EO) data in climate studies include, among others, analyses of the earths radiation budget (Earth Radiation Budget Experiment (ERBE), Scanner for Radiation Budget (ScaRaB), and Cloud and the Earths Radiant Energy System (CERES)), cloudiness (International Satellite Cloud Climatology Project (ISCCP)), vegetation research (Global Inventory Modeling and Mapping Studies (GIMMS)), and the National Oceanic and Atmospheric Administration – National Aeronautics and Space Administration (NOAA–NASA) Pathfinder Program. Despite several attempts, the great potential of the advanced very high resolution radiometer (AVHRR) 1 km satellite data for climate research remains substantially underutilized. To address this issue, the generation of a comprehensive satellite data archive of AVHRR data and products at 1 km spatial resolution over Canada for 1981–2004 (24 years) has been initiated, and a new system for processing at level 1B has been developed. This processing system was employed to generate baseline 1 day and 10 day year-round clear-sky composites for a 5700 km × 4800 km area of North America. This region is centred over Canada but also includes the northern United States, Alaska, Greenland, and surrounding ocean regions. The baseline products include top-of-atmosphere (TOA) visible and near-infrared reflectance, TOA band 4 and band 5 brightness temperature, a cloud – clear – shadow – snow and ice mask, and viewing geometry. Details of the data processing system are presented in the paper. An evaluation of the system characteristics and comparison with previous results demonstrate important improvements in the quality and efficiency of the data processing. The system can process data in a highly automated manner, both for snow-covered and snow-free scenes, and for daytime and nighttime orbits, with high georeferencing accuracy and good radiometric consistency for all sensors from AVHRR NOAA-6 to AVHRR NOAA-17. Other processing improvements include the implementation of advanced algorithms for clear sky – cloud – shadow – snow and ice scene identification, as well as atmospheric correction and compositing. At the time of writing, the assembled dataset is the most comprehensive AVHRR archive at 1 km spatial resolution over Canada that includes all available observations from AVHRR between 1981 and 2004. The archive and the processing system are valuable assets for studying different aspects of land, oceans, and atmosphere related to climate variability and climate change.

Trends in Land Evapotranspiration over Canada for the Period 1960–2000 Based on In Situ Climate Observations and a Land Surface Model

Abstract An assessment of annual trends in actual evapotranspiration (AET) and associated meteorological inputs is performed at 101 locations across Canada with available long-term hourly surface climate observations to determine if AET in Canada is increasing in relation to observed increases in air temperature. AET was estimated for the dominant land cover class, with representative soil and leaf area index conditions, within a 50 km × 50 km window around each location for the period 1960–2000. The Ecological Assimilation of Land and Climate Observations (EALCO) land surface model, which simulates coupled carbon, energy, and water cycles, was applied to estimate AET on a half-hourly basis at each location using in situ meteorological measurements and ambient atmospheric CO2 concentrations. Increases in annual AET, of up to 0.73% yr−1, were identified at 81 locations, and decreases, of up to 0.25% yr−1, were found at the remaining 20 stations. Statistically significant increasing trends were detected in ...

Comparison of International Panel on Climate Change Fourth Assessment Report climate model simulations of surface albedo with satellite products over northern latitudes

[1]xa0The surface albedos simulated by seventeen climate models over the northern latitudes of the Western Hemisphere were compared with satellite-derived albedo products provided by the International Satellite Cloud Climatology Project (ISCCP). Model simulations were conducted in support of the International Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Results show the following: (1) Annual albedo of the region averaged for all models is fairly close to that provided by the ISCCP (0.351 versus 0.334). The difference between model average and ISCCP albedos is well below the standard deviation in albedo among models. (2) Most models simulated seasonal variations in regional albedo reasonably well. In summer, the models systematically overestimated albedo relative to the ISCCP data by as much as 0.05. In winter, large differences were detected among the climate models. (3) The spatial correlations among models, and between models and ISCCP, depend on geographic location, season and surface type. In general, the spatial correlation coefficients between individual models and the ISCCP data were highest for the land surface in midsummer and for the ocean surface in spring. Model bias was smaller for the ocean surface than for the land surface, and smaller in summer than in winter. (4) Unlike the modeling results, the satellite data showed large interannual variations in albedo and a systematic decreasing trend over the 16 year period of 1984–1999. Depending on season, the standard deviation of albedo interannual variation ranged from 0.036 to 0.074, and the linear regression slope of the decreasing trend ranged from −0.02 to −0.05 per decade according to ISCCP results. The large interannual variation and decreasing trend are not reflected in model simulations. Additional efforts are still required to improve surface albedo simulations in GCMs and its mapping from satellite.

Modeling the Response of Canopy Stomatal Conductance to Humidity

Humidity of air is a key environmental variable in controlling the stomatal conductance (g) of plant leaves. The stomatal conductance‐humidity relationships employed in the Ball‐Woodrow‐Berry (BWB) model and the Leuning model have been widely used in the last decade. Results of independent evaluations of the two models vary greatly. In this study, the authors develop a new diagnostic parameter that is based on canopy water vapor and CO2 fluxes to assess the response of canopy g to humidity. Using eddy-covariance flux measurements at three boreal forest sites in Canada, they critically examine the performance of the BWB and the Leuning models. The results show that the BWB model, which employs a linear relationship between g and relative humidity (hs), leads to large underestimates of g when the air is wet. The Leuning model, which employs a nonlinear function of water vapor pressure deficit (Ds), reduced this bias, but it still could not adequately capture the significant increase of g under the wet conditions. New models are proposed to improve the prediction of canopy g to humidity. The best performance was obtained by the model that employs a power function of Ds, followed by the model that employs a power function of relative humidity deficit (1 2 hs). The results also indicate that models based on water vapor pressure deficit generally performed better than those based on relative humidity. This is consistent with the hypothesis that the stomatal aperture responds to leaf water loss because water vapor pressure deficit rather than relative humidity directly affects the transpiration rate of canopy leaves.

Spatiotemporal variations in land surface albedo across Canada from MODIS observations

A detailed knowledge of spatiotemporal variations in surface albedo is crucial if surface-atmosphere energy exchanges are to be accurately represented in climate models. Satellite observations can provide this information. This study uses moderate resolution imaging spectroradiometer (MODIS) data to investigate how summer and winter albedos, and the intra-annual variation in albedo, vary across the Canadian landscape. We show that (i) albedos generally decrease as one moves from grassland to broadleaved forest to needleleaved and mixed forest; (ii) the effects of snow on albedo vary among cover types; (iii) the largest intra-annual albedo variations occur over grasslands, cropland, and tundra; (iv) significant differences in albedo occur not only among broadleaved forest, needleleaf forest, grassland, and tundra, but also among their various canopy types (e.g., open versus closed canopies); and (v) land cover types sharing similar albedos in winter do not necessarily share similar albedos in summer. These trends are caused by differences in canopy structure and are supported to varying degrees by other in situ and remote sensing studies. These results suggest that the use of overly general land cover classes (e.g., needleleaved forest, grassland, tundra) in climate models will ignore important local-scale spatial variations in surface albedo.

A Method to Derive the Multispectral Surface Albedo Consistent with MODIS from Historical AVHRR and VGT Satellite Data

Abstract Multispectral surface albedo and bidirectional properties are required for accurate determination of the surface and atmosphere solar radiation budget. A method is developed here to obtain time series of these surface characteristics consistent with the Moderate Resolution Imaging Spectroradiometer (MODIS) using historical satellite observations with limited spectral coverage available from NOAA Advanced Very High Resolution Radiometer (AVHRR) and VEGETATION/Satellite pour l’Observation de la Terre (SPOT). A nonlinear regression model was developed that relates retrievals from four spectral channels of VEGETATION/SPOT or three spectral channels of NOAA AVHRR with retrieval from each of the seven MODIS channels designed for land applications. The model also takes into account the surface land cover type, the normalized difference vegetation index, and the seasonal cycle. It was applied to generate surface albedo and bidirectional parameters of the seven MODIS-like spectral channels at a 10-day int...

Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations

Flooding is projected to increase with climate change in many parts of the world. Floods in cold regions are commonly a result of snowmelt during the spring break-up. The peak river flow (Qpeak) for the Mackenzie River, located in northwest Canada, is modelled using the Gravity Recovery and Climate Experiment (GRACE) satellite observations. Compared with the observed Qpeak at a downstream hydrometric station, the model results have a correlation coefficient of 0.83 (p < 0.001) and a mean absolute error of 6.5% of the mean observed value of 28,400 m3·s−1 for the 12 study years (2003–2014). The results are compared with those for other basins to examine the difference in the major factors controlling the Qpeak. It was found that the temperature variations in the snowmelt season are the principal driver for the Qpeak in the Mackenzie River. In contrast, the variations in snow accumulation play a more important role in the Qpeak for warmer southern basins in Canada. The study provides a GRACE-based approach for basin-scale snow mass estimation, which is largely independent of in situ observations and eliminates the limitations and uncertainties with traditional snow measurements. Snow mass estimated from the GRACE data was about 20% higher than that from the Global Land Data Assimilation System (GLDAS) datasets. The model is relatively simple and only needs GRACE and temperature data for flood forecasting. It can be readily applied to other cold region basins, and could be particularly useful for regions with minimal data.

Forecasting Snowmelt-Induced Flooding Using GRACE Satellite Data: A Case Study for the Red River Watershed

Abstract. Flood forecasting of the spring freshet for cold-region watersheds where the discharge is predominately governed by snowpack accumulation and melting remains a challenge. A cold-region flood forecasting model is developed, using data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. The model forecasts flood by simulating peak surface runoff from snowmelt and the corresponding baseflow from groundwater discharge. Surface runoff is predicted from snowmelt, using a temperature index model. Baseflow is predicted, using a first order differential equation model. Streamflow measurement is used for model calibration. The model was applied to the Red River watershed, a USA–Canada transboundary basin located in central North America. The predicted flood compares well with the observed values at a downstream hydrometric station (r = 0.95). The result also reveals a 2-week hysteresis between the maximum snowmelt and the peak streamflow observed at the station. The model is relatively simple and needs only GRACE and temperature inputs for flood forecasting. It can be readily applied to other cold-region basins after simple calibration and could be particularly useful in regions with minimal data. For potential flood warning, the model also has the advantage of a much longer lead time than most traditional flood forecasting approaches.

Diurnal variation of direct and diffuse radiation and its impact on surface albedo

A method for calculating the diurnal distribution of direct and diffuse solar radiation was developed. Based on this method, we analysed the changing patterns of the two radiation components under different weather conditions. The results were then used to investigate the effect of underlying snow of a forest on the surface albedo. It shows that the albedo contribution from the underlying snow is stable on overcast days when the radiation is dominated by the diffuse component. However, on clear days when the radiation is dominated by the direct component, the diurnal change of albedo contributed from the snow is significant and it exhibits a w shape. It was also found that under any weather conditions, the vegetation masking effect is very significant. As a result, the albedo contribution from the underlying snow in the high latitude is much less than that obtained by the fractional average method commonly used in land surface schemes of climate models.

Using Satellite Remote Sensing to Assess and Monitor Ecosystem Integrity and Climate Change in Canadas National Parks

Natural Resources Canada, Parks Canada Agency and the University of Ottawa are developing standardized approaches for monitoring landscape change within and surrounding Canadas National Parks using Earth observation. This paper focuses on remote sensing methodologies developed at the CCRS for three types of ecological indicators: Landscape Pattern, Succession & Retrogression, and Net Primary Productivity (NPP), using La Mauricie National Park to demonstrate the methods and results. Landscape pattern analyses are discussed in relation to landscape metric stability, scaling, and selection. Major vegetation disturbances through time were examined using a hybrid change detection technique combining vegetation index differencing and constrained signature extension. Ecosystem productivity measures were developed using a remote sensing-based modeling approach known as EALCO (ecological assimilation of land and climate observations). It is anticipated that this pilot study will produce new automated EO processing methods that culminate in an operational remote sensing-based system for monitoring the ecological integrity of Canadas National Parks and their greater ecosystems.