Search Results
Abstract
It is well known that large lakes can perturb local weather and climate through mesoscale circulations, for example, lake effects on storms and lake breezes, and the impacts on fluxes of heat, moisture, and momentum. However, for both large and small lakes, the importance of atmosphere–lake interactions in northern Canada is largely unknown. Here, the Canadian Regional Climate Model (CRCM) is used to simulate seasonal time scales for the Mackenzie River basin and northwest region of Canada, coupled to simulations of Great Bear and Great Slave Lakes using the Princeton Ocean Model (POM) to examine the interactions between large northern lakes and the atmosphere. The authors consider the lake impacts on the local water and energy cycles and on regional seasonal climate. Verification of model results is achieved with atmospheric sounding and surface flux data collected during the Canadian Global Energy and Water Cycle Experiment (GEWEX) program. The coupled atmosphere–lake model is shown to be able to successfully simulate the variation of surface heat fluxes and surface water temperatures and to give a good representation of the vertical profiles of water temperatures, the warming and cooling processes, and the lake responses to the seasonal and interannual variation of surface heat fluxes. These northern lakes can significantly influence the local water and energy cycles.
Abstract
It is well known that large lakes can perturb local weather and climate through mesoscale circulations, for example, lake effects on storms and lake breezes, and the impacts on fluxes of heat, moisture, and momentum. However, for both large and small lakes, the importance of atmosphere–lake interactions in northern Canada is largely unknown. Here, the Canadian Regional Climate Model (CRCM) is used to simulate seasonal time scales for the Mackenzie River basin and northwest region of Canada, coupled to simulations of Great Bear and Great Slave Lakes using the Princeton Ocean Model (POM) to examine the interactions between large northern lakes and the atmosphere. The authors consider the lake impacts on the local water and energy cycles and on regional seasonal climate. Verification of model results is achieved with atmospheric sounding and surface flux data collected during the Canadian Global Energy and Water Cycle Experiment (GEWEX) program. The coupled atmosphere–lake model is shown to be able to successfully simulate the variation of surface heat fluxes and surface water temperatures and to give a good representation of the vertical profiles of water temperatures, the warming and cooling processes, and the lake responses to the seasonal and interannual variation of surface heat fluxes. These northern lakes can significantly influence the local water and energy cycles.
Abstract
Precipitation has a high spatial variability, and thus some modeling applications require high-resolution data (<10 km). Unfortunately, in some cases, such as meteorological forecasts and future regional climate projections, only spatial averages over large areas are available. While some attention has been given to the disaggregation of mean areal precipitation estimates, the computation of a disaggregated field with a realistic spatial structure remains a difficult task. This paper describes the development of a statistical disaggregation model based on Gibbs sampling. The model disaggregates 45.6-km-resolution rainfall fields to grids with pixel sizes ranging from 3.8 to 22.8 km. The model is conceptually simple, as the algorithm is straightforward to compute with only a few parameters to estimate. The rainfall depth at each grid pixel is related to the depths of the neighboring pixels, while the spatial variability is related to the convective available potential energy (CAPE) field. The model is developed using daily rainfall data over a 40 000-km2 area located in the southeastern United States. Four-kilometer-resolution rainfall estimates obtained from NCEP’s stage IV analysis were used to estimate the model parameters (2002–04) and as a reference to validate the disaggregated fields (2005/06). Results show that the model accurately simulates rainfall depths and the spatial structure of the observed field. Because the model has low computational requirements, an ensemble of disaggregated data series can be generated.
Abstract
Precipitation has a high spatial variability, and thus some modeling applications require high-resolution data (<10 km). Unfortunately, in some cases, such as meteorological forecasts and future regional climate projections, only spatial averages over large areas are available. While some attention has been given to the disaggregation of mean areal precipitation estimates, the computation of a disaggregated field with a realistic spatial structure remains a difficult task. This paper describes the development of a statistical disaggregation model based on Gibbs sampling. The model disaggregates 45.6-km-resolution rainfall fields to grids with pixel sizes ranging from 3.8 to 22.8 km. The model is conceptually simple, as the algorithm is straightforward to compute with only a few parameters to estimate. The rainfall depth at each grid pixel is related to the depths of the neighboring pixels, while the spatial variability is related to the convective available potential energy (CAPE) field. The model is developed using daily rainfall data over a 40 000-km2 area located in the southeastern United States. Four-kilometer-resolution rainfall estimates obtained from NCEP’s stage IV analysis were used to estimate the model parameters (2002–04) and as a reference to validate the disaggregated fields (2005/06). Results show that the model accurately simulates rainfall depths and the spatial structure of the observed field. Because the model has low computational requirements, an ensemble of disaggregated data series can be generated.
