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Joshua K. Roundy and Joseph A. Santanello

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Feedbacks between the land and the atmosphere can play an important role in the water cycle, and a number of studies have quantified land–atmosphere (LA) interactions and feedbacks through observations and prediction models. Because of the complex nature of LA interactions, the observed variables are not always available at the needed temporal and spatial scales. This work derives the Coupling Drought Index (CDI) solely from satellite data and evaluates the input variables and the resultant CDI against in situ data and reanalysis products. NASA’s Aqua satellite and retrievals of soil moisture and lower-tropospheric temperature and humidity properties are used as input. Overall, the Aqua-based CDI and its inputs perform well at a point, spatially, and in time (trends) compared to in situ and reanalysis products. In addition, this work represents the first time that in situ observations were utilized for the coupling classification and CDI. The combination of in situ and satellite remote sensing CDI is unique and provides an observational tool for evaluating models at local and large scales. Overall, results indicate that there is sufficient information in the signal from simultaneous measurements of the land and atmosphere from satellite remote sensing to provide useful information for applications of drought monitoring and coupling metrics.

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Dingwen Zeng, Xing Yuan, and Joshua K. Roundy

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Northeast China (NEC) suffered a severe drought that persisted from March to July of 2017 with profound impacts on agriculture and society, raising an urgent need to understand the mechanism for persistent droughts over midlatitudes. Previous drought mechanism studies focused on either large-scale teleconnections or local land–atmosphere coupling, while less attention was paid to their synergistic effects on drought persistence. Here we show that the 2017 NEC drought was triggered by a strong positive phase of the Arctic Oscillation in March, and maintained by the anticyclone over the area south to Lake Baikal (ASLB) through a quasi-stationary Rossby wave in April–July, accompanied by sinking motion and north wind anomaly. By using a land–atmosphere coupling index based on the persistence of positive feedbacks between the boundary layer and land surface, we find that the coupling states over NEC and ASLB shifted from a wet coupling in March to a persistently strengthened dry coupling in April–July. Over ASLB, the dry coupling and sinking motion increased surface sensible heat, decreased cloud cover, and weakened longwave absorption, resulting in a diabatic heating anomaly in the lower atmosphere and a diabatic cooling anomaly in the upper atmosphere. This anomalous vertical heating profile led to a negative anomaly of potential vorticity at low levels, indicating that the land–atmosphere coupling had a phase-lock effect on the Rossby wave train originating from upstream areas, and therefore maintained the NEC drought over downstream regions. Our study suggests that an upstream quasi-stationary wave pattern strengthened by land–atmosphere coupling should be considered in diagnosing persistent droughts, especially over northern midlatitudes.

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Joshua K. Roundy and Eric F. Wood

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Drought has significant social and economic impacts that could be reduced by preparations made possible through seasonal prediction. During the convective season, when the potential of extreme drought is the highest, the soil moisture can provide a means of improved predictability through land–atmosphere interactions. In the past decade, there has been a significant amount of work aimed at better understanding the predictability of land–atmosphere interactions. One such approach classifies the interactions between the land and the atmosphere into coupling states. The coupling states have been shown to be persistent and were used to demonstrate the existence of strong biases in the coupling of the NCEP Climate Forecast System, version 2 (CFSv2). In this work, the attribution of the coupling state on the seasonal prediction of precipitation and temperature and the extent to which the bias in the coupling state hinders the prediction of drought is analyzed. This analysis combines the predictions from statistical models with the predictions from CFSv2 as a means to isolate and attribute the predictability. The results indicate that the intermountain region is a hotspot for seasonal prediction because of local persistence of initial conditions. In addition, the local persistence of initial conditions provides some level of drought prediction; however, accounting for the spatial interactions provides a more complete prediction. Furthermore, the statistical models provide more skillful predictions of precipitation during drought than the CFSv2; however, the CFSv2 predictions are more skillful for daily maximum temperature during drought. The implication, limitations, and extensions of this work are also discussed.

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Joshua K. Roundy, Craig R. Ferguson, and Eric F. Wood

Abstract

Droughts represent a significant source of social and economic damage in the southeast United States. Having sufficient warning of these extreme events enables managers to prepare for and potentially mitigate the severity of their impacts. A seasonal hydrologic forecast system can provide such warning, but current forecast skill is low during the convective season when precipitation is affected by regionally varying land surface heat flux contributions. Previous studies have classified regions into coupling regimes based on the tendency of surface soil moisture anomalies to trigger convective rainfall. Until now, these classifications have been aimed at assessing the long-term dominant feedback signal. Sufficient focus has not been placed on the temporal variability that underlies this signal. To better understand this aspect of coupling, a new classification methodology suitable at daily time scales is developed. The methodology is based on the joint probability space of surface soil moisture, convective triggering potential, and the low-level humidity index. The methodology is demonstrated over the U.S. Southeast using satellite remote sensing, reanalysis, and hydrological model data. The results show strong persistence in coupling events that is linked to the land surface state. A coupling-based drought index shows good agreement with the temporal and spatial variability of drought and highlights the role of coupling in drought recovery. The implications of the findings for drought and forecasting are discussed.

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Hyo-Jong Song, Craig R. Ferguson, and Joshua K. Roundy

Abstract

The multimodel Global Land–Atmosphere Coupling Experiment (GLACE) identified the semiarid Southern Great Plains (SGP) as a hotspot for land–atmosphere (LA) coupling and, consequently, land-derived temperature and precipitation predictability. The area including and surrounding the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) SGP Climate Research Facility has in particular been well studied in the context of LA coupling. Observation-based studies suggest a coupling signal that is much weaker than modeled, if not elusive. Using North American Regional Reanalysis and North American Land Data Assimilation System data, this study provides a 36-yr (1979–2014) climatology of coupling for ARM-SGP that 1) unifies prior interdisciplinary efforts and 2) isolates the origin of the (weak) coupling signal. Specifically, the climatology of a prominent convective triggering potential–low-level humidity index (CTP–HIlow) coupling classification is linked to corresponding synoptic–mesoscale weather and atmospheric moisture budget analyses. The CTP–HIlow classification defines a dry-advantage regime for which convective triggering is preferentially favored over drier-than-average soils as well as a wet-advantage regime for which convective triggering is preferentially favored over wetter-than-average soils. This study shows that wet-advantage days are a result of horizontal moisture flux convergence over the region, and conversely, dry-advantage days are a result of zonal and vertical moisture flux divergence. In this context, the role of the land is nominal relative to that of atmospheric forcing. Surface flux partitioning, however, can play an important role in modulating diurnal precipitation cycle phase and amplitude and it is shown that soil moisture and sensible heat flux are significantly correlated with both occurrence and intensity of afternoon peak precipitation.

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Xing Yuan, Joshua K. Roundy, Eric F. Wood, and Justin Sheffield

Abstract

Seasonal hydrologic extremes in the form of droughts and wet spells have devastating impacts on human and natural systems. Improving understanding and predictive capability of hydrologic extremes, and facilitating adaptations through establishing climate service systems at regional to global scales are among the grand challenges proposed by the World Climate Research Programme (WCRP) and are the core themes of the Regional Hydroclimate Projects (RHP) under the Global Energy and Water Cycle Experiment (GEWEX). An experimental global seasonal hydrologic forecasting system has been developed that is based on coupled climate forecast models participating in the North American Multimodel Ensemble (NMME) project and an advanced land surface hydrologic model. The system is evaluated over major GEWEX RHP river basins by comparing with ensemble streamflow prediction (ESP). The multimodel seasonal forecast system provides higher detectability for soil moisture droughts, more reliable low and high f low ensemble forecasts, and better “real time” prediction for the 2012 North American extreme drought. The association of the onset of extreme hydrologic events with oceanic and land precursors is also investigated based on the joint distribution of forecasts and observations. Climate models have a higher probability of missing the onset of hydrologic extremes when there is no oceanic precursor. But oceanic precursor alone is insufficient to guarantee a correct forecast—a land precursor is also critical in avoiding a false alarm for forecasting extremes. This study is targeted at providing the scientific underpinning for the predictability of hydrologic extremes over GEWEX RHP basins and serves as a prototype for seasonal hydrologic forecasts within the Global Framework for Climate Services (GFCS).

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Joshua K. Roundy, Xing Yuan, John Schaake, and Eric F. Wood

Abstract

Hydrologic extremes in the form of flood and drought have large impacts on society that can be reduced through preparations made possible by seasonal prediction. However, the skill of seasonal predictions from global climate models is uncertain, which severely limits their practical use. In the past, the skill assessment has been limited to a single temporal or spatial resolution for a short hindcast period, which is prone to sampling errors, and noise that leads to uncertainty. In this work a framework that uses “canonical” forecast events, or averages in space–time, to provide a more certain assessment of when and where models are skillful is developed. This framework is demonstrated by using NCEP’s Climate Forecast System, version 2, hindcast dataset for precipitation and temperature over the contiguous United States (CONUS). As part of the canonical event analyses, the probabilistic predictability metric (PPM) is used to define spatial and seasonal variability of forecast skill and its attribution to El Niño–Southern Oscillation (ENSO) over the CONUS. The PPM indicates that there are clear seasonal and spatial patterns of model skill that provide a better understanding of when and where to have confidence in model predictions as compared to a skill metric based on a single temporal and spatial scale. Furthermore, the canonical event analysis also facilitates the attribution of spatiotemporal variations of precipitation predictive skill to the antecedent ENSO conditions. This work illustrates the importance of using canonical event analysis to diagnose seasonal predictions and discusses its extensions for model development.

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Xing Yuan, Eric F. Wood, Joshua K. Roundy, and Ming Pan

Abstract

There is a long history of debate on the usefulness of climate model–based seasonal hydroclimatic forecasts as compared to ensemble streamflow prediction (ESP). In this study, the authors use NCEP's operational forecast system, the Climate Forecast System version 2 (CFSv2), and its previous version, CFSv1, to investigate the value of climate models by conducting a set of 27-yr seasonal hydroclimatic hindcasts over the conterminous United States (CONUS). Through Bayesian downscaling, climate models have higher squared correlation R 2 and smaller error than ESP for monthly precipitation, and the forecasts conditional on ENSO have further improvements over southern basins out to 4 months. Verification of streamflow forecasts over 1734 U.S. Geological Survey (USGS) gauges shows that CFSv2 has moderately smaller error than ESP, but all three approaches have limited added skill against climatology beyond 1 month because of overforecasting or underdispersion errors. Using a postprocessor, 60%–70% of probabilistic streamflow forecasts are more skillful than climatology. All three approaches have plausible predictions of soil moisture drought frequency over the central United States out to 6 months, and climate models provide better results over the central and eastern United States. The R 2 of drought extent is higher for arid basins and for the forecasts initiated during dry seasons, but significant improvements from CFSv2 occur in different seasons for different basins. The R 2 of drought severity accumulated over CONUS is higher during winter, and climate models present added value, especially at long leads. This study indicates that climate models can provide better seasonal hydroclimatic forecasts than ESP through appropriate downscaling procedures, but significant improvements are dependent on the variables, seasons, and regions.

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Eleonora M. C. Demaria, Joshua K. Roundy, Sungwook Wi, and Richard N. Palmer

Abstract

The potential effects of climate change on the snowpack of the northeastern and upper Midwest United States are assessed using statistically downscaled climate projections from an ensemble of 10 climate models and a macroscale hydrological model. Climate simulations for the region indicate warmer-than-normal temperatures and wetter conditions for the snow season (November–April) during the twenty-first century. However, despite projected increases in seasonal precipitation, statistically significant negative trends in snow water equivalent (SWE) are found for the region. Snow cover is likely to migrate northward in the future as a result of warmer-than-present air temperatures, with higher loss rates in northern latitudes and at high elevation. Decreases in future (2041–95) snow cover in early spring will likely affect the timing of maximum spring peak streamflow, with earlier peaks predicted in more than 80% of the 124 basins studied.

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