Search Results

You are looking at 1 - 10 of 11 items for

  • Author or Editor: Jonghun Kam x
  • All content x
Clear All Modify Search
Jonghun Kam and Justin Sheffield

Abstract

This study evaluates wintertime drought and pluvial risk over California through a Bayesian analysis of the upper and lower quartile of PRISM-based precipitation from 1901 to 2015. Risk is evaluated for different time windows to estimate the impact of interannual and decadal-to-multidecadal Pacific and Atlantic variability [positive and negative phases of ENSO, Pacific decadal oscillation (PDO), and Atlantic multidecadal oscillation (AMO)]. The impact of increasing trends in global sea surface temperature (SST) on drought and pluvial risk is also examined with idealized experimental runs from three climate models [GFDL Atmospheric Model version 2.1 (AM2.1), CCM3, and GFS]. The results show that the influence of oceanic conditions on drought risk in California is significant but has changed with higher risk in the last half century, especially in Southern California. The influence of oceanic conditions on pluvial risk has also been significant, especially during the warm phase of the Pacific Ocean, but increases over the last century are small compared to drought. Results from the idealized climate model experiments show that natural variability likely played a major role in the observed changes in risk, with the global SST increasing trend possibly tempering the increases regionally but not significantly over California. Despite evolving preferential oceanic conditions for a pluvial event during the 2015/16 winter (positive phase of ENSO and PDO), California received an 11% winter precipitation surplus, which was not sufficient for drought recovery. The seasonal and longer-term outlook for negative phases of ENSO and PDO implies that drought risk will be elevated in Southern California for the next decade.

Full access
Jonghun Kam, Kimberly Stowers, and Sungyoon Kim

Abstract

This study introduces “Google Trends” as a social data source in monitoring and modeling the dynamics of drought awareness during the 2011–17 California drought. In this study, drought awareness is defined and operationalized as the relative search interest activities within California, using the search term “drought” from Google Trends. First, the 2011–17 California drought is characterized in the duration–intensity curve with other historical California droughts for comparative purposes, using the 12-month standard precipitation index data (1895–2017). Second, the potential triggers of the peaks of drought awareness during the 2011–17 California drought are investigated through Google Trends and Google Search. The Google Trends data show that the first peak of drought awareness occurred when the drought condition reached its peak and the governor declared the drought emergency (January 2014). The other peaks in August 2014, April 2015, and January 2017 are related to public interest in drought recovery driven by the forecast of the strong El Niño winter of 2014/15, the governor’s issue of water use rules, and California floods in early 2017, respectively. Last, a power-law decay model of drought awareness is fitted to the Google Trends data. According to the fitted power-law model, Californians remained interested in drought after the social trigger–related peaks longer than they did after the natural trigger–related peaks. The findings of this study suggest that it is necessary to develop a more realistic social dynamic modeling for communities that can respond to natural and human triggers and capture interactions with awareness of related disasters.

Full access
Jonghun Kam, Thomas R. Knutson, Fanrong Zeng, and Andrew T. Wittenberg
Full access
Thomas R. Knutson, Jonghun Kam, Fanrong Zeng, and Andrew T. Wittenberg
Open access
Jonghun Kam, Thomas R. Knutson, Fanrong Zeng, and Andrew T. Wittenberg
Open access
Jonghun Kam, Justin Sheffield, Xing Yuan, and Eric F. Wood

Abstract

To assess the influence of Atlantic tropical cyclones (TCs) on the eastern U.S. drought regime, the Variable Infiltration Capacity (VIC) land surface hydrologic model was run over the eastern United States forced by the North American Land Data Assimilation System phase 2 (NLDAS-2) analysis with and without TC-related precipitation for the period 1980–2007. A drought was defined in terms of soil moisture as a prolonged period below a percentile threshold. Different duration droughts were analyzed—short term (longer than 30 days) and long term (longer than 90 days)—as well as different drought severities corresponding to the 10th, 15th, and 20th percentiles of soil moisture depth. With TCs, droughts are shorter in duration and of a lesser spatial extent. Tropical cyclones variously impact soil moisture droughts via late drought initiation, weakened drought intensity, and early drought recovery. At regional scales, TCs decreased the average duration of moderately severe short-term and long-term droughts by less than 4 (10% of average drought duration per year) and more than 5 (15%) days yr−1, respectively. Also, they removed at least two short-term and one long-term drought events over 50% of the study region. Despite the damage inflicted directly by TCs, they play a crucial role in the alleviation and removal of drought for some years and seasons, with important implications for water resources and agriculture.

Full access
Jonghun Kam, Thomas R. Knutson, and P. C. D. Milly

Abstract

Over regions where snowmelt runoff substantially contributes to winter–spring streamflows, warming can accelerate snowmelt and reduce dry-season streamflows. However, conclusive detection of changes and attribution to anthropogenic forcing is hindered by the brevity of observational records, model uncertainty, and uncertainty concerning internal variability. In this study, the detection/attribution of changes in midlatitude North American winter–spring streamflow timing is examined using nine global climate models under multiple forcing scenarios. Robustness across models, start/end dates for trends, and assumptions about internal variability are evaluated. Marginal evidence for an emerging detectable anthropogenic influence (according to four or five of nine models) is found in the north-central United States, where winter–spring streamflows have been starting earlier. Weaker indications of detectable anthropogenic influence (three of nine models) are found in the mountainous western United States/southwestern Canada and in the extreme northeastern United States/Canadian Maritimes. In the former region, a recent shift toward later streamflows has rendered the full-record trend toward earlier streamflows only marginally significant, with possible implications for previously published climate change detection findings for streamflow timing in this region. In the latter region, no forced model shows as large a shift toward earlier streamflow timing as the detectable observed shift. In other (including warm, snow free) regions, observed trends are typically not detectable, although in the U.S. central plains we find detectable delays in streamflow, which are inconsistent with forced model experiments.

Full access
Jonghun Kam, Seung-Ki Min, Piotr Wolski, and Jong-Seong Kug
Full access
Jonghun Kam, Thomas R. Knutson, Fanrong Zeng, and Andrew T. Wittenberg
Full access
Xing Yuan, Eric F. Wood, Nathaniel W. Chaney, Justin Sheffield, Jonghun Kam, Miaoling Liang, and Kaiyu Guan

Abstract

As a natural phenomenon, drought can have devastating impacts on local populations through food insecurity and famine in the developing world, such as in Africa. In this study, the authors have established a seasonal hydrologic forecasting system for Africa. The system is based on the Climate Forecast System, version 2 (CFSv2), and the Variable Infiltration Capacity (VIC) land surface model. With a set of 26-yr (1982–2007) seasonal hydrologic hindcasts run at 0.25°, the probabilistic drought forecasts are validated using the 6-month Standard Precipitation Index (SPI6) and soil moisture percentile as indices. In terms of Brier skill score (BSS), the system is more skillful than climatology out to 3–5 months, except for the forecast of soil moisture drought over central Africa. The spatial distribution of BSS, which is similar to the pattern of persistency, shows more heterogeneity for soil moisture than the SPI6. Drought forecasts based on SPI6 are generally more skillful than for soil moisture, and their differences originate from the skill attribute of resolution rather than reliability. However, the soil moisture drought forecast can be more skillful than SPI6 at the beginning of the rainy season over western and southern Africa because of the strong annual cycle. Singular value decomposition (SVD) analysis of African precipitation and global SSTs indicates that CFSv2 reproduces the ENSO dominance on rainy season drought forecasts quite well, but the corresponding SVD mode from observations and CFSv2 only account for less than 24% and 31% of the covariance, respectively, suggesting that further understanding of drought drivers, including regional atmospheric dynamics and land–atmosphere coupling, is necessary.

Restricted access