Examinando por Autor "Arevalo, Jorge"

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  • Miniatura
    Ítem
    Implementation of Snowpack Treatment in the CPC Water Balance Model and Its Impact on Drought Assessment
    (American Meteorological Society, 2021) Arevalo, Jorge; Welty, Josh; Fan, Yun; Zeng, Xubin
    Droughts are a worldwide concern, thus assessment efforts are conducted by many centers around the world, mainly through simple drought indices, which usually neglect important hydrometeorological processes or require variables available only from complex land surface models (LSMs). The U.S. Climate Prediction Center (CPC) uses the Leaky Bucket (LB) water-balance model to postprocess temperature and precipitation, providing soil moisture (SM) anomalies to assess drought conditions. However, despite its crucial role in the water cycle, snowpack has been neglected by LB and most drought indices. Taking advantage of the high-quality snow water equivalent (SWE) data from The University of Arizona (UA), a single-layer snow scheme, forced by daily temperature and precipitation only, is developed for LB implementation and tested with two independent forcing datasets. Compared against the UA and SNOTEL SWE data over CONUS, LB outperforms a sophisticated LSM (Noah/NLDAS-2), with the median LB versus SNOTEL correlation (RMSE) about 40% (26%) higher (lower) than that from Noah/NLDAS-2, with only slight differences due to different forcing datasets. The changes in the temporal variability of SM due to the snowpack treatment lead to improved temporal and spatial distribution of drought conditions in the LB simulations compared to the reference U.S. Drought Monitor maps, highlighting the importance of snowpack inclusion in drought assessment. The simplicity but reasonable reliability of the LB with snowpack treatment makes it suitable for drought monitoring and forecasting in both snow-covered and snow-free areas, while only requiring precipitation and temperature data (markedly less than other water-balance-based indices).
  • Miniatura
    Ítem
    Quantifying the Occurrence of Record Hot Years Through Normalized Warming Trends
    (American Geophysical Union, 2021) Zeng, Xubin; Reeves Eyre, J. E. Jack; Dixon, Ross D.; Arevalo, Jorge
    Surface air temperature trends and extreme events are of global concern and they are related. Here, we show that the occurrence of record hot years over different latitudes from 1960 to 2019 are more strongly correlated with the observational annual mean temperature trends normalized by internal variability. Compared with the raw trends showing Arctic amplification, the normalized trends show a tropical amplification over land. Two hot spots with more frequent occurrence of record hot years are identified: northern hemisphere ocean (vs. land) and southern hemisphere tropical land (vs. mid- and high-latitude lands). Ensemble mean results from 32 Earth system models agree with observations better than individual models, but they do not reproduce observed large differences in correlations across latitudes between normalized trends and record-breaking events over land versus ocean. Our results enable the quantification of record hot year occurrence through normalized warming trends and provide new metrics for model evaluation and improvement.