3 Search Results

Phenology shifts influence regional climate by altering energy, and water fluxes through biophysical processes. However, a quantitative understanding of the phenological control on vegetation’s biophysical feedbacks to regional climate remains elusive. Using long-term remote sensing observations and Weather Research and Forecasting (WRF) model simulations, we investigated vegetation phenology changes from 2003 to 2020 and quantified their biophysical controls on the regional climate in Northeast China. Our findings elucidated that earlier green-up contributed to a prolonged growing season in forests, while advanced green-up and delayed dormancy extended the growing season in croplands. This prolonged presence and increased maximum green cover intensifiedmore » climate-vegetation interactions, resulting in more significant surface cooling in croplands compared to forests. Surface cooling from forest phenology changes was prominent during May’s green-up (-0.53 ± 0.07 °C), while crop phenology changes induced cooling throughout the growing season, particularly in June (-0.47 ± 0.15 °C), July (-0.48 ± 0.11 °C), and September (-0.28 ± 0.09 °C). Furthermore, we unraveled the contributions of different biophysical pathways to temperature feedback using a two-resistance attribution model, with aerodynamic resistance emerging as the dominant factor. Crucially, our findings underscored that the land surface temperature (LST) sensitivity, exhibited substantially higher values in croplands rather than temperate forests. These strong sensitivities, coupled with the projected continuation of phenology shifts, portend further growing season cooling in croplands. These findings contribute to a more comprehensive understanding of the intricate feedback mechanisms between vegetation phenology and surface temperature, emphasizing the significance of vegetation phenology dynamics in shaping regional climate pattern and seasonality.« less

The major characteristics of ice microphysics in Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) bulk-type cloud microphysics originate from the diagnosed ice number concentration, which is a function of the cloud-ice mixing ratio. In this study, we correct numerical errors in ice microphysics processes of the WDM6, in which the cloud-ice shape is assumed as single bullets and examine the impact on regional climate simulations. Here, by rederiving the relationships between cloud microphysics characteristics, including the one linking the cloud-ice mixing ratio and number concentration, we remove numerical errors intrinsic to the description of cloud-ice characteristics in the originalmore » WDM6 microphysics scheme. The revised WDM6 is tested using a WRF framework for regional climate simulations over the East Asian region. We find that our correction to the WDM6 improves the model’s performance in capturing the observed distribution of the monsoon rain band. A reduction in cloud ice is significant in the revised WDM6, which strengthens the Western North Pacific High. By conducting the additional sensitivity experiment in which the characteristics of cloud-ice shape are revised as the one for the column type, our study also finds out that the impacts of the existing numerical errors on the simulated monsoon is as large as the ones of the changes in cloud-ice shape.« less

Offshore wind resource assessments for the conterminous U.S. and Hawai’i have been developed before, but Alaska’s offshore wind resource has never been rigorously assessed. Alaska, with its vast coastline, presents ample potential territory in which to build offshore wind farms, but significant challenges have thus far limited Alaska’s deployment of utility-scale wind energy capacity to a modest 62 MW (or approximately 2.7% of the state’s electric generation) as of this writing, all in land-based wind farms. This study provides an assessment of Alaska’s offshore wind resource, the first such assessment for Alaska, using a 14-year, high-resolution simulation from a numericalmore » weather prediction and regional climate model. This is the longest-known high-resolution model data set to be used in a wind resource assessment. Widespread areas with relatively shallow ocean depth and high long-term average 100-m wind speeds and estimated net capacity factors over 50% were found, including a small area near Alaska’s population centers and the largest transmission grid that, if even partially developed, could provide the bulk of the state’s energy needs. The regional climate simulations were validated against available radiosonde and surface wind observations to provide the confidence of the model-based assessment. The model-simulated wind speed was found to be skillful and with near-zero average bias (-0.4–0.2 m s^-1) when averaged over the domain. Small sample sizes made regional validation noisy, however.« less