Showing papers by "Shih-Yu Wang published in 2016"

Anthropogenic footprint of climate change in the June 2013 northern India flood

Abstract: During 13–17 June 2013, heavy rainfall occurred in the northern Indian state of Uttarakhand and led to one of the worst floods in history and massive landslides, resulting in more than 5000 casualties and a huge loss of property. In this study, meteorological and climatic conditions leading up to this rainfall event in 2013 and similar cases were analyzed for the period of 1979–2012. Attribution analysis was performed to identify the natural and anthropogenic influences on the climate anomalies using the historical single-forcing experiments in the Coupled Model Intercomparison Project Phase 5. In addition, regional modeling experiments were carried out to quantify the role of the long-term climate trends in affecting the rainfall magnitude of the June 2013 event. It was found that (a) northern India has experienced increasingly large rainfall in June since the late 1980s, (b) the increase in rainfall appears to be associated with a tendency in the upper troposphere towards amplified short waves, and (c) the phasing of such amplified short waves is tied to increased loading of green-house gases and aerosols. In addition, a regional modeling diagnosis attributed 60–90 % of rainfall amounts in the June 2013 event to post-1980 climate trends.

The 2011 Great Flood in Thailand: Climate Diagnostics and Implications from Climate Change

Abstract: Severe flooding occurred in Thailand during the 2011 summer season, which resulted in more than 800 deaths and affected 13.6 million people. The unprecedented nature of this flood in the Chao Phraya River basin (CPRB) was examined and compared with historical flood years. Climate diagnostics were conducted to understand the meteorological conditions and climate forcing that led to the magnitude and duration of this flood. Neither the monsoon rainfall nor the tropical cyclone frequency anomalies alone was sufficient to cause the 2011 flooding event. Instead, a series of abnormal conditions collectively contributed to the intensity of the 2011 flood: anomalously high rainfall in the premonsoon season, especially during March; record-high soil moisture content throughout the year; elevated sea level height in the Gulf of Thailand, which constrained drainage; and other water management factors. In the context of climate change, the substantially increased premonsoon rainfall in CPRB after 1980 and the...

Synoptic and quantitative attributions of the extreme precipitation leading to the August 2016 Louisiana flood

Abstract: The catastrophic August 2016 flood in the U.S. state of Louisiana was a result of intense precipitation produced by a slow-moving tropical low pressure system interacting with an eastward-traveling baroclinic trough to the north. While tropical-midlatitude interactions of this nature are rare, they are not unprecedented. Analyses presented point towards the tendency for more and perhaps stronger upper level troughs propagating out of the western U.S. in summer; these then have an increasing potential to cross paths with low pressure systems that form around the Gulf Coast. Combined with the projected increase in precipitable water, resulting precipitation magnitude would increase. Large-ensemble modeling indicates that the prospect of future tropical-midlatitude interactions is a scenario that Louisiana will face in the future, while regional simulations suggest that the climate warming since 1985 may have increased the event precipitation (11-14 August 2016) on the order of 20%, all of which allude to a conceivable forecast of non-hurricane related, warm season extreme precipitation in the Gulf Coast states.

Indications for Protracted Groundwater Depletion after Drought over the Central Valley of California

Abstract: Ongoing (2014–16) drought in the state of California has played a major role in the depletion of groundwater. Within California’s Central Valley, home to one of the world’s most productive agricultural regions, drought and increased groundwater depletion occurs almost hand in hand, but this relationship appears to have changed over the last decade. Data derived from 497 wells have revealed a continued depletion of groundwater lasting a full year after drought, a phenomenon that was not observed in earlier records before the twenty-first century. Possible causes include 1) lengthening of drought associated with amplification in the 4–6-yr drought and El Nino frequency since the late 1990s and 2) intensification of drought and increased pumping that enhances depletion. Altogether, the implication is that current groundwater storage in the Central Valley will likely continue to diminish even further in 2016, regardless of the drought status.

Evaluation of the AMPS Boundary Layer Simulations on the Ross Ice Shelf with Tower Observations

Abstract: Flight operations in Antarctica rely on accurate weather forecasts aided by the numerical predictions primarily produced by the Antarctic Mesoscale Prediction System (AMPS) that employs the polar version of the Weather Research and Forecasting (Polar WRF) Model. To improve the performance of the model’s Mellor–Yamada–Janjic (MYJ) planetary boundary layer (PBL) scheme, this study examines 1.5 yr of meteorological data provided by the 30-m Alexander Tall Tower! (ATT) automatic weather station on the western Ross Ice Shelf from March 2011 to July 2012. Processed ATT observations at 10-min intervals from the multiple observational levels are compared with the 5-km-resolution AMPS forecasts run daily at 0000 and 1200 UTC. The ATT comparison shows that AMPS has fundamental issues with moisture and handling stability as a function of wind speed. AMPS has a 10-percentage-point (i.e., RH unit) relative humidity dry bias year-round that is highest when katabatic winds from the Byrd and Mulock Glaciers excee...

The 2014/15 Snowpack Drought in Washington State and its Climate Forcing

Abstract: Introduction. The state of Washington declared a drought emergency in May 2015 following a drastic decline in snowpack over the adjoining Cascades (Fig. 5.1a). Unlike past droughts that were mainly caused by precipitation deficits (e.g., the 2005 drought; Anderson et al. 2006), the 2014/15 cold season (November–March) produced near-normal precipitation statewide (Fig. 5.1b). In what has since been nicknamed the “snowpack drought” of 2015 (www.ecy.wa.gov/drought/), the drought was more a result of unprecedented warmth (Fig. 5.1c) that caused cold-season precipitation to fall as rain rather than snow on the mountains. A small change in temperature can alter the water balance by reducing the precipitation falling as snow, which results in declined snow water equivalent and summer streamflow (Mote 2006; Stewart et al. 2004). This 2014/15 situation thereby sets an example for the known effect of atmospheric warming on reducing mountain snowpack in the Pacific Northwest (PNW), a known risk that has been reported by a sizable body of research (e.g., Stoelinga et al. 2010; Mote et al. 2014; Abatzoglou et al. 2014). Reduction in the PNW snowpack also increases the risk of wildfires, the latter of which is evidenced by the remarkable 2015 wildfire season, the largest in the state’s history. A Washington Department of Agriculture report (http://agr.wa.gov/FP/Pubs /docs/104-495InterimDroughtReport2015.pdf ) estimates the 2015 drought alone has caused more than $335 million (U.S. dollars) of loss for the state’s agricultural industry. In this study, we investigate the role natural climate variability played in the 2015 Washington state drought and situate it in the context of anthropogenic climate change.

A combined dynamical and statistical downscaling technique to reduce biases in climate projections: an example for winter precipitation and snowpack in the western United States

Abstract: Large biases associated with climate projections are problematic when it comes to their regional application in the assessment of water resources and ecosystems. Here, we demonstrate a method that can reduce systematic biases in regional climate projections. The global and regional climate models employed to demonstrate the technique are the Community Climate System Model (CCSM) and the Weather Research and Forecasting (WRF) model. The method first utilized a statistical regression technique and a global reanalysis dataset to correct biases in the CCSM-simulated variables (e.g., temperature, geopotential height, specific humidity, and winds) that are subsequently used to drive the WRF model. The WRF simulations were conducted for the western United States and were driven with (a) global reanalysis, (b) original CCSM, and (c) bias-corrected CCSM data. The bias-corrected CCSM data led to a more realistic regional climate simulation of precipitation and associated atmospheric dynamics, as well as snow water equivalent (SWE), in comparison to the original CCSM-driven WRF simulation. Since most climate applications rely on existing global model output as the forcing data (i.e., they cannot re-run or change the global model), which often contain large biases, this method provides an effective and economical tool to reduce biases in regional climate downscaling simulations of water resource variables.