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Xing Yuan, Linying Wang, Shanshan Wang, Peng Ji and Miao Zhang Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China ([email protected]) The changes and predictability of droughts across scales Introduction Causes for a meteorological drought Changes in flash (agricultural) droughts over China Looking forward: hyper-resolution modeling Sub-seasonal to seasonal drought forecasting Drought was a climate anomaly that occurred naturally, affected a large area and persist for a long time. However, climate change and human intervention have altered the characteristics of drought, and increased the society’s vulnerability to drought. Drought has now covered a variety of spatiotemporal scales from seasonal/decadal droughts at regional to continental scales that are basically associated with large-scale climate anomalies, to flash droughts at local scale that are usually concurrent with heat extremes. Drought also has quite different implications across a number of sectors, with considerations augmented from meteorological drought to agricultural and hydrological droughts. This raises a grand challenge to understand the changes and predictability of droughts across scales. The presentation will be started by diagnosing an El Niño-induced meteorological drought happened over northern China last year, followed by detecting the changes in flash agricultural droughts over China during the past three decades, exploring the human influence on hydrological droughts over Yellow River, investigating drought predictability at sub-seasonal to seasonal scales, and it will be concluded by showing a hyper-resolution land surface modeling for advancing drought simulation and forecasting in the future. Symposium on Obervation and Modeling across the Scales, June 2-3, 2016, Princeton, NJ, USA • Flash droughts are most likely to occur over humid and semi-humid regions. • There are increasing trends for flash droughts over different regions in China. • The increasing trends do not decline after the big El Niño event in 1997/98, but the warming hiatus does exist over many regions of China. References References ØYuan, X.*, Roundy, J. K., Wood, E. F., and Sheffield, J.: Seasonal forecasting of global hydrologic extremes: system development and evaluation over GEWEXbasins, Bull. Am. Meteorol. Soc., 96, 1895-1912, doi:10.1175/BAMS-D-14-00003.1, 2015. ØYuan, X.,* Wood, E. F., and Ma, Z.: A review on climate-model-based seasonal hydrologic forecasting: physical understanding and system development, WIREs Water, 2, 523-536, doi: 10.1002/wat2.1088, 2015. ØYuan, X.*, Ma, Z., Pan, M., and Shi, C.: Microwave remote sensing of short-term droughts during crop growing seasons, Geophys. Res. Lett., 42, 4394– 4401, doi:10.1002/2015GL064125, 2015. ØYuan X.*, et al.: An experimental seasonal hydrological forecasting system over the Yellow River basin-Part I: Understanding the role of initial hydrological conditions. Hydrology and Earth System Sciences, revised, 2016. ØYuan X.*: An experimental seasonal hydrological forecasting system over the Yellow River basin-Part II: The added value from climate forecast models. Hydrology and Earth System Sciences, revised, 2016. ØWang L., X. Yuan*, Z. Xie, P. Wu, Y. Li: Increasing flash droughts over China during the recent global warming hiatus. Scientific Report, revised, 2016. ØJi P., X. Yuan*, and X.-Z. Liang: Hyper-resolution modelling of soil moisture and land fluxes over a mountainous area by using a Conjunctive Surface- Subsurface Process (CSSP) land surface model, to be submitted, 2016. ØWang S., X. Yuan*, and Y. Li: Causes and predictability for the 2015 Northern China drought, in preparation, 2016. Hydrological droughts over Yellow River drought • 2015: a powerful El Niño event; • Pacific SST anomaly is similar to Fig.2a; • Changes the atmospheric circulation on local and Asia via teleconnection (e.g., WPSH locates to east than normal); • Moisture cannot reach the N China, and induces an extreme drought. Summary Figure 1 Spatial distributions for precipitation anomalies for July in 2015 based on climatology (1982–2010) from 2K+ station observational data (Unit: mm) Figure 2 Heterogeneous correlation map of the first mode of the MCA for the detrended and normalized (a) July SST and (b) July precipitation during 1979–2015 • El Niño-like SST pattern corresponds the drought in NC and wet in SC Figure 3 (a) 500hPa HGT and 850 hPa wind for July 2015, (b) for climatology 1982-2010 and (c) their differences,(d) SST anomaly of July 2015 . Figure 4 Ensemble mean frequency of flash drought events Figure 5 Interannual and decadal variations of ensemble mean flash drought event and its component variables averaged over China (Left) Figure 6 Mann-Kendall trends of flash drought event and its component variables of temperature (T), soil moisture (SM) and ET (Right) Fig. 15 Observed and simulated daily soil moisture (m 3 /m 3 ) in 20cm depth at four stations during 2004-2013. Fig. 17 Spatial distribution of annual mean soil moisture between 0-0.1 and 0.1-1m at 90m resolution • The Conjunctive Surface-Subsurface Process Model (CSSP) simulates soil moisture well • Accuracy of forcing precipitation is important in hyper-resolution soil moisture modelling • Lateral exchange of soil water can be neglected even at 1km resolution but is important at 90m resolution, while surface water lateral transport may has its effects even at coarse resolution Fig. 16 Spatial distribution of annual mean surface soil moisture at three resolution. Figure 7 The Yellow River Basin Figure 8a Correlation between standardized river discharges and SPI at different time scales (Left) Figure 8b The same as Fig. 8a, but for the correlations at different calendar months (Right) • Meteorological drought developing to hydrological drought: 4 months • The accumulated (1-12 months) rainfall in autumn mainly affects hydrological process • Human influence on the severity of hydrological drought is 16 times larger than natural effect Fig. 9 Anomalies in discharge in Yellow River Fig. 10 Flowchart for the experimental seasonal hydrological forecasting system over the Yellow River Fig. 11 Maximum lead time (months) where the initial conditions prevail over the meteorological forcings (RMSEESP/RMSErevESP<1) in the streamflow predictability Fig. 12 Spatial distributions of average AC of ensemble mean forecasts from ESP/VIC (left panel) and NMME/VIC (right panel) for monthly soil moisture at different leads Fig. 13 The Root Mean Squared Error Skill Score (SSRMSE) for streamflow as a function of start month and lead time at twelve hydrological gauges. Lead Day p(y 1 |o 1 ) 1 0.43 6 0.25 11 0.14 16 0.10 21 0.07 26 0.06 31 0.06 Lead Day p(y 1 |o 1 ) 1 0.57 6 0.37 11 0.25 16 0.15 21 0.12 26 0.11 31 0.10 China South Fig. 14 Detectabilityfor flash drought with different lead time • NCEP/CFSR is used as reference. • NCEP/CFSv2 captured 30-50% of flash droughts over southern China at 6-day lead. • The system draws from a legacy of a global hydrological forecasting system (Yuan et al., BAMS, 2015) that is able to make use of real-time seasonal climate predictions from North American Multimodel Ensemble (NMME) climate models through a statistical downscaling approach, but with a higher resolution and a spatially disaggregated calibration procedure over the Yellow River basin. The changes and predictability of droughts across scales have been investigated: ØLarge-scale climate anomaly (e.g., El Niño) triggered the 2015 NC drought via PJ teleconnection; ØThere was an increasing trend in flash droughts over China during the global warming hiatus; ØHuman intervention intensified YR hydrological droughts 16 times larger than climate anomaly; ØClimate forecast models added value to drought prediction at sub-seasonal to seasonal scales; ØHyper-resolution land surface modeling with the consideration of lateral surface and sub-surface flow would advance the drought modeling and prediction across scales.
