Role of Moisture Transport and Recycling in Characterizing Droughts: Perspectives from Two Recent U.S

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Role of Moisture Transport and Recycling in Characterizing Droughts: Perspectives from Two Recent U.S. Droughts and the CFSv2 System

a b a,c d TIRTHANKAR ROY, J. ALEJANDRO MARTINEZ, JULIO E. HERRERA-ESTRADA, YU ZHANG, e a f,g a FRANCINA DOMINGUEZ, ALEXIS BERG, MIKE EK, AND ERIC F. WOOD a Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey b Escuela Ambiental, Universidad de Antioquia, Medellin, Colombia c Department of Earth System Science, Stanford University, Stanford, California d One Concern, Inc., Palo Alto, California e Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, Illinois f NOAA/National Centers for Environmental Prediction/Environmental Modeling Center, College Park, Maryland g National Center for Atmospheric Research, Boulder, Colorado

(Manuscript received 4 August 2018, in ﬁnal form 19 November 2018)

ABSTRACT

We investigate the role of moisture transport and recycling in characterizing two recent drought events in Texas (2011) and the Upper Midwest (2012) by analyzing the precipitation, evapotranspiration, precipitable water, and soil moisture data from the Climate Forecast System version 2 (CFSv2) analysis. Next, we evaluate the CFSv2 forecasts in terms of their ability to capture different drought signals as reflected in the analysis data. Precipitation from both sources is partitioned into recycled and advected components using a moisture accounting–based precipitation recycling model. All four variables reflected drought signals through their anomalously low values, while precipitation and evapotranspiration had the strongest signals. Drought in Texas was dominated by the differences in moisture transport, whereas in the Upper Midwest, the absence of strong precipitation-generating mechanisms was a crucial factor. Reduced advection from the tropical and midlatitude Atlantic contributed to the drought in Texas. The Upper Midwest experienced reduced contributions from recycling, terrestrial sources, the midlatitude Pacific, and the tropical Atlantic. In both cases, long-range moisture transport from oceanic sources was reduced during the corresponding drought years. June and August in Texas and July and August in the Upper Midwest were the driest months, and in both cases, drought was alleviated by the end of August. Moisture from terrestrial sources most likely contributed to alleviating drought intensity in such conditions, even with negative anomalies. The forecasts showed noticeable differences as compared to the analysis for multiple variables in both regions, which could be attributed to several factors as discussed in this paper.

1. Introduction energy impacts, navigation problems, and environmental losses (Grigg 2014) and endangering international a. Recent droughts in Texas and the Upper Midwest grain markets (Boyer et al. 2013). Severe drought associated with high temperatures In Texas and the northern part of Mexico, drought affected Texas and the Upper Midwest during the started emerging in the fall of 2010, due primarily to a La summer of 2011 and 2012 (Karl et al. 2012). In the Niña event in the tropical Pacific Ocean (Seager et al. central United States, the severity of the drought was 2014). The forecast models predicted transient eddy the worst since the Dust Bowl of the 1930s (Schubert et al. moisture flux divergence related to a northward shift of 2004), causing crop failures, job losses, water shortages, the Pacific–North American storm track; however, the observed drought was related to mean flow moisture divergence and drying over the southern plains and Supplemental information related to this paper is available southeastern United States, as a result of negative North at the Journals Online website: https://doi.org/10.1175/JHM-D- Atlantic Oscillation pattern during the 2010/11 winter 18-0159.s1. (Seager et al. 2014). Texas experienced record dry conditions from March to August in 2011, with much Corresponding author: Tirthankar Roy, [email protected] lower 12-month rainfall total (October 2010–September

DOI: 10.1175/JHM-D-18-0159.1 Ó 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). 140 JOURNAL OF HYDROMETEOROLOGY VOLUME 20

2011) as compared to the climatology (statewide driest therefore were suitable only for monthly or seasonal 5% with the exception of north and east Dallas), and time scales. Zangvil et al. (2004) proposed a recycling summer temperatures reaching close to the warmest model that could be applied to a daily time scale, which (over 58F above the long-term average) statewide tem- showed the importance of relaxing the assumption of perature ever recorded in the United States (Nielson- negligible moisture storage. Dominguez et al. (2006) Gammon 2012). proposed a two-dimensional precipitation recycling Hoerling et al. (2014) argued that the 2012 drought model, termed the Dynamic Recycling Model (DRM), over the central plains was primarily due to the natural based on the conservation of atmospheric water vapor variations in weather, triggered by the absence of and relaxing the assumption of negligible moisture rainfall-producing mechanisms, such as the springtime storage, which was later extended by Martinez and low pressure systems and the associated warm and cold Dominguez (2014) to account for the relative contri- fronts that usually result in widespread rains or thun- butions of different sources to a given sink. The DRM derstorms and cause the majority of precipitation in has been a popular choice recently for studying the char- July and August. The Gulf of Mexico moisture trans- acteristics of precipitation recycling in different parts of port was reduced, following decreased cyclones and the world (Bisselink and Dolman 2008; DeAngelis et al. frontal activity in late spring, while the upper tropo- 2010; Pathak et al. 2017; Hua et al. 2016, 2017; Agudelo sphere sustained the anomalous high pressure that in- et al. 2018; Mei et al. 2015). hibited the formation of convective rainfall due to c. Objectives increased subsidence and stability of the atmosphere (Hoerling et al. 2014). Mallya et al. (2013) argued that The role of precipitation recycling in drought occur- La Niña caused weaker winter storms in the United rence and drought dynamics has been a growing area States during 2011, which resulted in the 2012 drought of research (Dominguez et al. 2009; Herrera-Estrada in the Upper Midwest. et al. 2017; Roundy et al. 2013; Herrera-Estrada 2017). Although previous studies have looked at the b. Precipitation recycling potential causes of the recent droughts in Texas and The contribution of evapotranspiration from a re- the Upper Midwest, the role of precipitation recycling gion to the same region’s precipitation is defined as in influencing different characteristics of these drought precipitation/moisture recycling. The recycling ratio (ratio events has just begun to be explored (Herrera-Estrada between recycled precipitation and total precipitation) 2017). This study is twofold. In the first part, we use is used as a diagnostic to quantify the strength of land– the DRM to investigate the role of precipitation re- atmosphere interactions or coupling. Precipitation recycling and advection in characterizing the 2011 and cycling can be a key component of land–atmosphere 2012 droughts in Texas and the Upper Midwest. We coupling, which plays a crucial role in determining the do this using the data from NOAA/NCEP’s Climate extent and severity of extreme climatic conditions such Forecast System version 2 (CFSv2), which is a cutting- as droughts (Findell and Eltahir 2003). Depending on edge coupled atmosphere–ocean–land surface–sea ice the geographic location, time of year, and the spatial modeling system applied at a high resolutiononaglobal scale of modeling, the recycled precipitation can be scale (Saha et al. 2014). In the second part, we investigate as high as 25% of the total precipitation (Dominguez how well the CFSv2 forecasts capture different drought et al. 2008). signals for the two events and the potential causes behind Modeling atmospheric moisture transport to identify the discrepancies. different sources and sinks can be classified into three different categories: analytical or box models, numer- 2. Methods and data ical water vapor tracers, and physical water vapor a. Climate Forecast System forecast and analysis tracers, that is, isotopes [see Gimeno et al. (2012) for a comprehensive review]. In this study, we use an ana- CFSv2 (Saha et al. 2014) is a high-resolution, cou- lytical modeling approach. Analytical recycling models pled atmosphere–ocean–land surface–sea ice modeling have evolved over the last several decades, starting system applied on a global scale. It has improved data as- from one-dimensional models (Savenije 1995; Budyko similation and forecast model components as compared to 1974) to more detailed, higher-dimensional repre- its predecessor, that is, CFSv1 (Saha et al. 2006), and was sentations (Burde and Zangvil 2001; Bo et al. 1994; made operational by NCEP in March 2011. The atmo- Brubaker et al. 1993; Schär et al. 1999; Trenberth 1999). sphere, ocean, land surface, and sea ice models have 64, 40, Oneofthemajorshortcomingsofearliermodelswas 4, and 3 levels, respectively. The initial conditions for the that they all assumed negligible moisture storage, and forecasting model are derived from a coupled reanalysis JANUARY 2019 R O Y E T A L . 141 " ð # t over 32 years (1979–2010), which ensures consistent 0 E(x, y, t) and stable calibrations and skill estimates for seasonal R(x, y, t) 5 1 2 exp 2 dt t W(x, y, t) and subseasonal predictions (Saha et al. 2014). Note 2 5 a that after NCEP discontinued the production of CFS 0(x, y, t)R1(x, y, t) Reanalysis (CFSR) in April 2011, CFSv2 Operational 1 a (x, y, t)a (x, y, t)R (x, y, t), (1) Analysis [Climate Data Assimilation System (CDAS)] 0 1 2 was made available. Therefore, in this study, we com- where E is the amount of evaporation, W is the amount of bine both CFSR and CFSv2 operational analysis data to precipitable water, R1 and R2 are related to the local col- cover the entire analysis period (2010–13) and use the lections of moisture from region 1 (local source and sink) term ‘‘CFSv2 analysis’’ interchangeably to indicate ei- and region 2 (external source); a1 accounts for the fraction ther of these datasets. of moisture produced in region 2 that is not precipitated in

In this study, we use six CFS variables, namely, pre- region 1. In addition a1(x, y, t) 5 1 2 R1(x, y, t). The term cipitation, latent heat flux, precipitable water, soil moisture, a0 is defined as equal to 1, and it is introduced in order to specific humidity, zonal wind speed, and meridional wind extend the notation of the indices in Eqs. (1) and (2).More speed. The first four variables are extracted from the sur- details about the R and a terms are presented in Martinez face flux files (Gaussian grid; T574 for analysis and T126 for and Dominguez (2014).AccordingtotheDRM,thefrac- forecasts), whereas the last three variables are from the tion of moisture collected by an air column from a given atmospheric files with 3D pressure-level data (latitude– region along the portion of trajectory l, which finally rea- 8 8 longitude grid; 0.5 for analysis and 1.0 for forecasts). The ches a target region at t0,isgivenby temporal resolution of the datasets is 6 h. Analysis fields were " # l21 available for all the variables except precipitation, for which 5 a cl(x, y, t) P j(x, y, t) Rl(x, y, t). (2) the initial conditions (0-h forecasts) were used (see Table S1 j50 in the online supplemental material). CFSv2 forecast ensembles are based on different initialization times. Thus, at It is possible to have multiple trajectories within a single any given time, some ensemble members are newer while region, in which case, the net contribution of any region some are older based on when they were initialized. We Ak can be expressed as the sum of the contributions from analyze 20 CFSv2 ensemble forecasts during the time period the individual trajectories within that region: of June–September (JJAS) for Texas in 2011 and the Upper Midwest in 2012. There are five different initialization days 5 å ak(x, y, t) cl(x, y, t) , (3) l2A (11, 16, 21, 26, and 31 May) with four initialization times per k day (0, 16, 12, and 18 h). CFSv2 analysis variables are up- a a scaled from 0.58 to 1.08 spatial resolution using barycentric where k (not ) is the fraction of moisture on each grid linear interpolation with Delaunay triangulation to match cell within the domain, which originated as evaporation the resolution of the forecasts. from region Ak. So, the total fraction of atmospheric moisture collected across NA regions can be written as b. Precipitation recycling modeling NA Precipitation recycling is modeled in this study using the 5 å R(x, y, t) ak(x, y, t) . (4) DRM, which is based on water vapor accounting in a 2D k51 semi-Lagrangian framework. Compared to the other 2D Following are some key points that need to be taken into models, the DRM relaxes the assumption of negligible at- account while using the DRM: mospheric moisture storage, and thus, is applicable to the daily time scale. The model is based on the precipitable 1) The recycling ratio within a grid cell can be modeled water budget and its changes due to precipitation, evapo- either in terms of precipitable water or precipitation. In ration, and the convergence of the vertically integrated the DRM, the recycling ratio at any grid cell is the same moisture flux. As such, the model has been useful for for precipitable water and precipitation, following the studying first-order water vapor transport at the continental assumption of a well-mixed atmosphere. However, this scale. does not hold true when regional averages are calcu- According to the DRM, the fraction of atmospheric lated. This is due to the fact that not all of the available moisture collected by an air column along its trajectory precipitable water ends up as precipitation. Therefore, while passing through two adjacent regions (from time t2 to the spatial structures of these two variables are also t1 in region 2 and from t1 to t0 in region 1, where t0 repre- different. In this study, we focus on regional averages of sents the present time, t1 is previous to t0,andt2 is previous both the variables to get a complete picture of the to t1)isgivenby moisture distribution. 142 JOURNAL OF HYDROMETEOROLOGY VOLUME 20

2) The DRM does not account for all the actual pre- over the land surface. The model provides recycling and cipitable water or precipitation within a given region advection components of precipitation in each of these because some of this moisture can originate outside 32 regions. In this study, the regions of interest (sinks) the domain under consideration. This is not a critical are region 14 (R14) and region 8 (R08), which we refer issue for regions that are in the middle of the domain to as ‘‘Texas’’ and ‘‘Upper Midwest,’’ respectively. since most of the moisture there comes from around d. Workflow the vicinity, which is already included in the domain. However, the regions near the boundaries of the To study the role of precipitation recycling, we run the main domain show much less moisture accounting DRM with the CFSv2 analysis data for three consecu- as a result of not factoring in the sources outside the tive years, with the drought year placed in the middle. domain (see Fig. S1). Thus, for Texas, we run the model from 2010 to 2012 3) The two-dimensional approximation of the DRM (drought year 2011) and for the Upper Midwest from has been a subject of debate (Van Der Ent and 2011 to 2013 (drought year 2012). For CFSv2 forecasts, Savenije 2013; Goessling and Reick 2013), particu- we specifically focus on the respective drought years larly in regions of strong shear. However, the model (2011 for Texas and 2012 for the Upper Midwest). The has proven to work well for larger domains (e.g., La study involves 44 different simulations using the DRM Plata River basin in South America), without signif- model (four year-long simulations with CFSv2 analysis icant wind shear (Dominguez et al. 2006; Martinez for 2010–13 and 20 summer runs with CFSv2 forecasts and Dominguez 2014). separately for 2011 and 2012). All the variables except 4) The DRM requires effective 2D zonal and meridio- for zonal and meridional winds are aggregated to the nal wind as inputs to calculate recycling. In the daily level. Wind components are used at 6-hourly time analytical solution that leads to the DRM, these step to capture the diurnal variability. The spatial reso- winds correspond to the vertically integrated mois- lution is set at 1.08 for all the analyses carried out in this ture flux vectors, divided by the precipitable water. study. The CFSv2 analysis data from May are used as the However, we find that this approach tends to ignore spinup for the CFSv2 summer (JJAS) forecasts. Local the effects of topographic barriers and overestimates moisture recycling, as well as moisture advection, is moisture transport in regions where lower level calculated from the model runs for each day. atmospheric circulation acts as a dominant moisture transport mechanism. Therefore, we implement an 3. Results and discussion alternative approach, where the 2D effective wind vector is calculated by taking the weighted average of In this section, we present the results from the diagnostic the wind vectors at 1000, 850, and 700 hPa pressure study of the two drought events as well as the comparison levels, following the method developed by Wimmers of CFSv2 analysis and forecasts. Drought years in this and Velden (2011). This method was originally ap- study are identified as having anomalously low pre- plied to interpolate precipitable water fields over the cipitation/evapotranspiration/soil moisture or a combina- ocean, using wind fields from an atmospheric model tion thereof, in comparison to the preceding and following (Wimmers and Velden 2011). In our case, the use of years. Thus, for Texas, we consider 2011 as the drought the same method, even over the land, for obtaining year and 2010 and 2012 as the nondrought years. Likewise, effective 2D wind fields resulted in a more realistic in the Upper Midwest, 2012 is considered to be the drought spatial distribution of atmospheric moisture trans- year and 2011 and 2013 as the nondrought years. port patterns as compared to the vertical integration a. CFSv2 analysis precipitation approach. Additionally, this version of the DRM was able to account for more moisture for each region We use the CFSv2 analysis data both for drought (see Fig. S2). diagnostics as well as forecast comparison. Figure 2 compares summer (JJAS) precipitation total from six c. Study regions different sources: CFSv2 analysis or CFSR, Modern- The first step in running the DRM involves the de- Era Retrospective Analysis for Research and Appli- lineation of the study regions. Figure 1 shows the 32 cations (MERRA; Rienecker et al. 2011), the European regions constructed in this study in such a manner that Centre for Medium-Range Weather Forecasts (ECMWF) they subdivide the continent into smaller regions (es- interim reanalysis (ERA-Interim; Dee et al. 2011), pecially at the core of the continent), while the regions the Japanese 55-year Reanalysis (JRA-55; Kobayashi over the oceans have larger areas. This is useful to better et al. 2015), Global Precipitation Climatology Centre capture the more complex moisture circulation patterns (GPCC; Ziese et al. 2014) gridded observations, and JANUARY 2019 R O Y E T A L . 143

FIG. 1. The 32 regions designed in this study for running the DRM. Note that in the way the regions are deﬁned in this study, they do not completely correspond to the state boundaries. We call region 14 (R14) the ‘‘Texas Region,’’ which includes parts of Texas, Louisiana, Mississippi, and Mexico. The ‘‘Upper Midwest Region’’ is represented by region 8 (R08), which includes parts of Minnesota, South Dakota, Wisconsin, Nebraska, Kansas, Iowa, Missouri, and Illinois.

Parameter-Elevation Regressions on Independent Slopes is strongly reflected in evapotranspiration and soil mois- Model (PRISM; Daly et al. 1994) gridded observations. ture. Drought signal is reflected in the soil moisture as JRA-55 precipitation is in general higher for both regions early as February for this region. The cumulative pre- as compared to the other products. CFSv2 analysis shows cipitation and precipitable water plots of Fig. 3 for Texas lower precipitation as compared to the other products show a decrease in both variables, and so the drought was (mainly in the Upper Midwest) until around 2004, after more likely related to moisture transport. Internal at- which it lies well within the interproduct spread. Clearly, mospheric variability causing mean flow moisture di- all the precipitation products show the lowest amount of vergence and drying (Seager et al. 2014) might have summer precipitation in 2011 for Texas and 2012 for the played a key role in determining the moisture transport Upper Midwest. The spatial precipitation field of CFSv2 pattern during this period. The drought in Texas was analysis also compares well with the precipitation from initiated in the fall of 2010 (Seager et al. 2014) but became the North American Land Data Assimilation System much stronger during the following year (see Fig. 2). phase 2 (NLDAS-2; Mitchell 2004) and the recently In the Upper Midwest, drought did not emerge until developed Multi-Source Weighted-Ensemble Precipitation May/June, and the duration of the event was also shorter (MSWEP; Beck et al. 2017, 2019).SeeFig.S3for as compared to the Texas drought. This agrees with the monthly comparisons of the three products. findings of Hoerling et al. (2014), who characterized the event as a ‘‘flash drought.’’ As can be seen in Fig. S4, b. Analysis of hydrometeorological variables the difference between drought and nondrought year Figure 3 shows the cumulative plots of precipitation, pre- evapotranspiration is largest during the months of July and cipitable water, evapotranspiration, and soil moisture from August. Soil moisture shows similar characteristics; how- the CFSv2 analysis for Texas and the Upper Midwest during ever, the drought signal is reflected much earlier (April). 2010–12 and 2011–13, respectively. Note that although they Strong drought signal is also seen in precipitation during are storage variables, we plotted the cumulative values of July and August. Some amount of precipitation occurred precipitable water as well as soil moisture just to identify the during the end of June and August, but the overall differences more clearly. The time series plots of these var- precipitation was low as compared to the other years. iables (without accumulations) are provided in Fig. S4. Precipitable water shows no or little anomaly, although In both regions, drought has been reflected in the anoma- there was a significant decrease in precipitation (Fig. 3). lously low values of all four variables, with precipitation and This suggests that the negative precipitation anomaly evapotranspiration showing the strongest signals. was largely influenced by the absence of precipitation- As reflected in the time series of precipitation and generating mechanisms, which is in line with the findings of evapotranspiration (Fig. S4), in Texas, the month of April Hoerling et al. (2014), who, along with the reduced Gulf of happens to be the onset of the drought event in 2011. Mexico moisture transport, identified reduced cyclone The entire summer season (JJAS) looks dry except for and frontal activity and inhibition of summer convection as some small rainfall events in July and September. June and the key reasons behind the drought in the central Great August appear to be the driest months. Precipitation signal Plains. During 2012 in the Upper Midwest, precipitation 144 JOURNAL OF HYDROMETEOROLOGY VOLUME 20

FIG. 2. Summer (JJAS) precipitation total from CFSv2 analysis (CFSR), MERRA, ERA- Interim, JRA-55, GPCC, and PRISM. Data from Climate Reanalyzer (http://cci-reanalyzer. org), Climate Change Institute, University of Maine. was reduced early, although evapotranspiration did not recycling (R14). Several regions contributed moisture to the drop until late June. Soil moisture was likely a contributor Upper Midwest, including recycling, neighboring regions to evapotranspiration until this point, beyond which it [including parts of the northern United States (R03) and the was too low for sustaining evapotranspiration. In early Southwest (R10)], southern Canada (R20), and the tropical April, soil moisture started decreasing and, unlike the Atlantic (R28). Clearly, recycling and terrestrial evapo- nondrought years, did not recover due to low precipitation. transpiration are important sources of moisture for the Evapotranspiration reached the minimum value by the Upper Midwest, which is reasonable given the location of end of June and the lowest values persisted until the end of the region (surrounded by landmass as opposed to oceans). summer, when the drought was alleviated by increased During the drought, the amount of precipitation from precipitation. recycling decreased in proportion (%) in the Upper Midwest (Fig. 4), and to a lesser extent in Texas. While it c. Analysis of recycling and advection may appear intuitive, it is not necessarily trivial, since Figure 4 shows the moisture contributions from the dif- moisture recycling expressed as a fraction of total pre- ferent regions defined in Fig. 1 to precipitation in Texas and cipitation has been shown to be sometimes negatively the Upper Midwest as calculated with the DRM (similar correlated to precipitation (Dominguez and Kumar figure with precipitable water is given in Fig. S5). The pat- 2008). In other words, in the Upper Midwest, when total terns of relative and absolute advection and recycling were precipitation decreases, moisture recycling can actually similar for the most part in both of these regions, with a few be high. This is because advection is the main driver of exceptions (e.g., advection from R28 to Texas). Although (high) precipitation, and when advection becomes recycling decreased during the respective drought years, the weaker, the overall precipitation amount decreases. relative contributions of all the sources were similar for all Evapotranspiration from local sources (i.e., recycling) the years in both regions. A similar figure created using then becomes important to sustain low precipitation. precipitable water instead of precipitation, which shows Furthermore, recycling ratios change as a function of similar results, is shown in Fig. S5. the area of the region of interest (Dominguez et al. In Texas, the primary moisture contribution was from 2006; Dirmeyer and Brubaker 2007). In our case, both the tropical Atlantic (R28), which is consistent with the recycling and advection are seen to decrease during the seasonal location of the Bermuda high pressure system, drought years both in terms of recycling ratio (relative) affecting moisture flows into the southern United States and recycled amount (absolute). (Wang et al. 2010; Kam et al. 2014). The next big sources of Figure 5 shows spatially the advected and recycled moisture for Texas were the midlatitude Atlantic (R26) and precipitation components (mm) during the summer JANUARY 2019 R O Y E T A L . 145

FIG. 3. Cumulative plots of precipitation, precipitable water, evapotranspiration, and soil moisture in Texas during 2010–12 and the Upper Midwest during 2011–13 in the CFSv2 analysis.

(JJAS) of the three consecutive years in both Texas and shown in Fig. S7. Precipitation recycling was noticeably the Upper Midwest regions (a similar figure with pre- lower than usual in both regions, especially during the cipitable water is shown in Fig. S6). The green box summer of the corresponding drought years. In Texas, represents the area of interest, for which the advected during the drought year (2011), precipitation recycling and recycled components are calculated. In Texas, the started decreasing from mid-April and remained low all contributions from the midlatitude Atlantic (R26) and throughout the summer. In the Upper Midwest, re- recycling (R14) were reduced during the drought year. cycling was low throughout the drought year (2012), Small contributions from the Pacific that were seen in except for some peaks in December, when the drought 2010 also disappeared in the following years. The post- had already weakened. drought year showed similar spatial patterns as the For Texas, the most significant source of precipitation drought year but with larger magnitudes. In the Upper was advection from tropical and midlatitude Atlantic Midwest, contributions from the tropical Atlantic (R28), (R26 and R28), the contribution of which was signifi- midlatitude Pacific (R25), and recycling were reduced cantly low since the beginning of the drought year (2011) during the 2012 summer. Contrary to the Texas drought, until late summer. The high advection during the end of which involved primarily reduced advection from oce- summer alleviated the drought event during that year. anic sources and reduced recycling, several land regions The next significant sources of precipitation were local (e.g., the western United States) also played a crucial recycling and advection from the contiguous United role in characterizing the Upper Midwest drought. States (CONUS; excluding R14). While recycling, as Figure 6 shows the temporal patterns of local re- well as advection from the Atlantic, reduced significantly cycling and advection from the corresponding significant during the drought year, CONUS advection did not sources (different for Texas and the Upper Midwest) alter much. Thus, it could be argued that precipitation during the summer of 2010–13. For clarity, Fig. 6 shows from terrestrial sources did not significantly influ- cumulative amounts; the absolute time series plots are ence the drought event. In fact, this precipitation more 146 JOURNAL OF HYDROMETEOROLOGY VOLUME 20

FIG. 4. Advection from different source regions (see Fig. 1) to Texas and the Upper Midwest derived from the CFSv2 analysis, expressed both as fractions of annual precipitation (%; top graph in each panel) and absolute amounts (mm; bottom graph in each panel). Values for Texas and the Upper Midwest, indicated by the black boxes, correspond to the recycling ratio (%) and recycled precipitation (mm). Note that the sum of the percentages of all regions in any given year is not necessarily 100%, since the DRM does not account for all the moisture as a result of losses across the boundaries. likely played a role in alleviating the drought intensity precipitation from the CONUS during 2011 higher than in to some extent, which was characterized primarily by 2010 and 2012. However, this was not enough to keep long-range moisture transport (from oceanic sources) Texas away from a drought. This highlights the importance and recycling. Precipitation from terrestrial origin of upwind land regions for precipitation and suggests that a collected during the late winter (January–February) weaker role of these sources could have made the summer was nearly enough to sustain accumulated terrestrial drought even worse. precipitation to similar levels observed in nondrought The main contributors of precipitation in the Upper years, with interesting contributions from the South- Midwest were recycling, tropical Atlantic, midlatitude west (R10) and Upper Midwest (R08). However, pre- Pacific, and terrestrial sources. Unlike Texas, the con- cipitation recycling in Texas and advected precipitation tribution from the terrestrial sources also decreased from the Atlantic were lower during the drought year during the drought year in this case (can be seen in and dominated the precipitation negative anomalies Figs. 4–6). Recycling started decreasing as early as over the region. Advection from the northern and March but the advection from the CONUS (excluding southwestern United States (R03, R08, and R10) was R08) started decreasing in June. As a result, the onset of seen to slightly increase during the drought year; how- drought, in this case, was late compared to Texas (see ever, the magnitude was much smaller as compared to Fig. 3). During 2012, advection from the midlatitude the dominant moisture sources. The generalized de- Pacific was significant during the spring as well as the crease in moisture transport from different regions is preceding (2011) winter. However, it decreased during consistent with the general decrease in convergence re- the summer of 2012. The contribution from the tropical ported by Seager et al. (2014) for Texas. However, despite Atlantic was low throughout the spring and summer of the overall decrease, even since the fall of 2010 (Seager et al. 2012, but it increased during the end of summer. 2014), we found that terrestrial sources contributed more Hoerling et al. (2014) reported a reduction in precipi- precipitation to Texas during the winter prior to the tation related to environments less favorable to pre- summer drought compared with the same contribution cipitation, including high pressure between May and during the nondrought years. Specifically, our estimates August. We also found a general decrease in the pre- suggest a larger contribution from the CONUS during cipitation contributed from different regions to the January–February, which kept the accumulated contributed Upper Midwest, especially during the summer of 2012. JANUARY 2019 R O Y E T A L . 147

FIG. 5. Spatial patterns of recycled and advected summer precipitation over Texas and the Upper Midwest from CFSv2 analysis (note the difference in units for the two regions).

However, we also note that contributions from the August (see Fig. S8). This could be related to the lack midlatitude Pacific (likely related to synoptic activity) of details/processes in the land surface model (Noah) were equal or larger during 2012 compared to 2011 and within the CFS system, which results in unre- 2013 between January and May and accumulated pre- stricted transpiration, even in dry conditions. The prob- cipitation from the CONUS shows the largest decline lem exacerbates during summertime as transpiration is after June, in contrast to the sharp decrease in contri- increased to account for the temperature bias. The mean butions from recycling and tropical Atlantic since April. precipitable water is similar for both forecasts and analysis in Texas, whereas in the Upper Midwest, the d. Comparison of forecasts and analysis forecasts show overestimation, which is consistent with In this section, we compare the CFSv2 analysis and the overestimation of precipitation. The temporal pro- forecast data to see how well the forecasts were able files of soil moisture from the analysis and forecasts to capture different drought signals. In particular, we match to some extent for the Upper Midwest. However, compare the hydrometeorological variables (precipita- in Texas, almost all the ensemble members underesti- tion, evapotranspiration, precipitable water, and soil mate soil moisture. In Texas, for all four variables, moisture) as well as the recycled and advected pre- the more recent forecast ensemble members (darker cipitation components for both products (Figs. 7, 8). curves) in general tend to be lower than the respective For Texas, the ensemble mean precipitation from the ensemble means, while in the Upper Midwest, the newer forecasts closely follows the analysis. In general, the ensemble members match better the ensemble mean. In more recent forecasts (darker gray) tend to underesti- all cases, the variability of the ensemble members de- mate precipitation, while the earlier forecasts (light creases with the initialization time, that is, less vari- gray) tend to overestimate precipitation, while com- ability in the newer ensembles and vice versa. pared against the precipitation from the analysis. On the As can be seen in Fig. 8, the recycled and advected other hand, in the Upper Midwest, all the forecasts precipitation components from the analysis data are well significantly overestimate precipitation. Evapotranspi- captured by the forecast ensemble during the summer of ration is overestimated in both regions, but the differ- Texas. However, in the Upper Midwest, both of these ences are more striking for the Upper Midwest, where components are significantly overestimated by the significant overestimation is evident during July and forecasts during the same period, following the similar 148 JOURNAL OF HYDROMETEOROLOGY VOLUME 20

FIG. 6. Cumulative plots of recycled and advected precipitation over Texas and the Upper Midwest from the CFSv2 analysis (note the difference in the scales for the two regions). behavior of total precipitation (Fig. 7). Similar to what during the drought year (2012) summer, these ratios is seen in Fig. 7, the newer ensemble members for both were about 8.45 and 3.76, respectively. The low values advected and recycled precipitation in general stay of the ratio in case of forecasts are attributed to larger below the respective ensemble means in Texas, while in local evapotranspiration produced by the forecasts the Upper Midwest, the newer members show better (Fig. 8), which is more striking in the Upper Midwest. match with the ensemble means. The variability among In Texas, the ratio of advected analysis and advected the ensemble members also decreases with the initial- forecasts is 1.14 and the ratio of recycled analysis and ization time (less variability in newer members). recycled forecasts is 0.96, which indicate that the To get a better idea about the partitioning of total forecasts and analysis are in good agreement. In con- precipitation into recycled and advected components trast, for the Upper Midwest, the same are 0.45 and in both analysis and forecasts, we calculated some di- 0.20, respectively. These large differences are also ev- agnostic ratios from the DRM results, as shown in ident from Fig. 8. In this case, both advected and re- Table 1. Note that the ensemble mean is used for cal- cycled components are overestimated by the forecasts, culating the ratios for the forecasts. During the drought but the recycled one relatively more so. The ratio of year (2011) summer in Texas, the advected precipita- advected analysis and recycled analysis is higher for tion was about 9.21 times higher than the recycled pre- both regions during the drought years (compared to cipitation for the analysis, while the same ratio was 7.77 the preceding and following years), and these differ- times higher for the forecasts. In the Upper Midwest, ences are also more striking in the Upper Midwest. JANUARY 2019 R O Y E T A L . 149

FIG. 7. Comparison of cumulative precipitation, evapotranspiration, precipitable water, and soil moisture from CFSv2 analysis and forecasts. Darker gray dots represent newer forecast ensemble members based on the time of initialization.

This implies that although precipitation was low the tropical Atlantic was higher as compared to the land during the drought years (Fig. 3), the relative fraction sources (0.50 vs 0.66), however, this behavior reversed of advected precipitation increased as compared to for the following year (0.64 vs 0.48). The more significant recycled precipitation. oceanic moisture source in the Upper Midwest was The next set of diagnostic ratios was calculated to the midlatitude Pacific, the contributions of which de- compare the advection contributions from the oceanic creased more during the drought year as compared to and terrestrial sources during the drought and normal the land sources, and this pattern was similar for both (preceding and following) years. In general, the ratios the preceding and following years (0.29 vs 0.66 in the are smaller for the preceding year in both regions, im- preceding year and 0.38 vs 0.48 in the following year), plying that the following year was relatively drier as suggesting the partial shutdown of long-range moisture compared to the preceding year. In Texas, the fractional transport, similar to Texas. decrease in contributions from the tropical and mid- We also studied if the skill of the forecasts measured latitude Atlantic was larger as compared to the land in terms on normalized mean bias (NMB) and Pearson’s sources (0.41 vs 0.79 for the preceding year and 0.55 vs linear correlation coefficient R improves with the time 0.92 for the following year), which suggests the partial of initialization (see Fig. S10). NMB tends to decrease shutdown of long-range moisture sources. In such con- with the initialization time (more recent forecasts are ditions, land sources can actually assuage drought in- associated with lower NMB and vice versa) for pre- tensity, even with negative anomalies. In the Upper cipitation in Texas. However, in the Upper Midwest, Midwest, the fractional decrease in contributions from there is no clear pattern of improvement. The estimates 150 JOURNAL OF HYDROMETEOROLOGY VOLUME 20

FIG. 8. Comparison of advected and recycled precipitation (cumulative) from CFSv2 forecasts and analysis. Darker gray dots represent newer ensemble members. Note the differences in y-axis scale between panels. of NMB stabilize with the initialization time in both While this provides some overall idea about the poten- cases. Correlation coefﬁcient R does not show any no- tial skill of the forecasts, the analysis presented here, by ticeable increasing or decreasing trend, although the no means, can or is meant to substitute a rigorous as- most recent forecasts (initialized at 1800 UTC 31 May) sessment of forecasting skill based on the initialization correspond to the highest value of the metric for Texas and lead times of forecasts. precipitation. NMB for evapotranspiration improves in e. Implications of the results Texas but does not change much in the Upper Midwest. For evapotranspiration, R has a slightly decreasing trend The two drought events considered in this study dif- with the initialization time in Texas and a slightly in- fered in their underlying mechanisms, which also likely creasing trend in the Upper Midwest. NMB of pre- contributed to the differences in the performance of the cipitable water and soil moisture does not change much forecasts. Although moisture transport from the oceanic in both regions. The R value tends to increase with the sources was reduced in both cases, the Upper Midwest initialization time for precipitable water and to decrease had more prominent contributions from the land sur- with soil moisture in Texas. Note that we only studied a face. Both the advected and recycled components of small subset of the total forecast ensemble in this case. precipitation were overestimated in the Upper Midwest.

TABLE 1. Diagnostic ratios calculated for the summer (JJAS) from DRM results.

Ratios Texas Upper Midwest Advection and recycling contributions Drought year Advected analysis/recycled analysis 9.21 8.45 Advected forecasts/recycled forecasts 7.77 3.76 Advected analysis/advected forecasts 1.14 0.45 Recycled analysis/recycled forecasts 0.96 0.20 Preceding year Advected analysis/recycled analysis 8.31 3.99 Following year Advected analysis/recycled analysis 6.90 4.88 Oceanic and terrestrial contributions Drought year and preceding year Recycling drought/recycling normal 0.38 0.30 CONUS drought/CONUS normal 0.79 0.66 Atlantic drought/Atlantic normal 0.41 0.50 Pacific drought/Pacific normal — 0.29 Drought year and following year Recycling drought/recycling normal 0.45 0.32 CONUS drought/CONUS normal 0.92 0.48 Atlantic drought/Atlantic normal 0.55 0.64 Pacific drought/Pacific normal — 0.38 JANUARY 2019 R O Y E T A L . 151

The inconsistencies in the forecast precipitation could could be a result of an increased forecast of evapo- be attributed to several factors, including the represen- transpiration in the region. Several other factors can tation of atmospheric processes (e.g., microphysics and also affect the soil moisture forecasts, which is already cumulus parameterizations), surface processes (land difficult to simulate in the land surface models (Koster surface model), and land–atmosphere interactions (land– et al. 2009; Dirmeyer et al. 2018). The observations of atmosphere coupling within the model). For example, this variable are scarce and associated with large un- Jiang et al. (2009) showed that adding an interactive certainties which make benchmarking and improvement canopy model and a simple groundwater model to Noah of land surface models more challenging (Dirmeyer results in improved summer precipitation simulation in et al. 2016). Comprehensive studies in this direction the central United States. In another example, Anyah et al. (e.g., Dirmeyer et al. 2016, 2018) have found that global (2008), using the land surface model Land–Ecosystem– land surface models, like Noah in the CFS, have biases Atmosphere Feedback (LEAF) with groundwater dy- not only on their mean values of soil moisture but also namics (LEAF-Hydro), found that a simple groundwater on their seasonality, interannual variability (standard scheme was associated with more soil moisture and pre- deviation), and memory (decorrelation time). The au- cipitation over parts of the Upper Midwest and Texas thors of those studies suggest that only longer time series during September. A fully coupled groundwater– of soil moisture measurements, both from in situ and atmosphere module can significantly improve the rep- satellite estimates, would help to improve the simula- resentations of water partitioning within the modeling tions of characteristics like interannual variability and system. The overestimation of forecast advection in the memory traits of soil moisture. These aspects could be atmospheric component of the CFS could be due to the important in the simulation of variations at the sea- errors in the forecasting of atmospheric circulation sonal and longer time scales. In terms of model com- patterns or disturbances several weeks in advance, ponents, this variability involves the feedbacks and bringing, for example, more moisture from the tropical interactions between land surface fluxes, soil moisture, Atlantic compared to the atmospheric analysis. shallow groundwater, runoff, and seasonal snowpack. Evapotranspiration was overestimated by the fore- We note that validation and benchmarking of land casts in both cases, but more so in the Upper Midwest. surface models for the simulation and forecast of cli- This could be attributed to the unrestricted evapo- mate extremes like droughts can be even more chal- transpiration in the land surface model component lenging, given the rarity and extreme characteristics of within the CFSv2 system. It could be that the model these events. keeps evaporating from the deep soil to meet the warm bias, resulting in excessive evapotranspiration over the 4. Summary and conclusions summer period. The model might have reached deeper soil layers in the Upper Midwest, thereby causing In this study, we investigate the 2011 drought in Texas larger overestimation of evapotranspiration in this re- and 2012 drought in the Upper Midwest by analyzing gion. Note that in the Noah model, the root zone for all several hydrometeorological variables from CFSv2 types of vegetation is the entire soil column, which can analysis data and forecasts. We use the DRM to study lead to overestimation of transpiration by vegetation the recycled and advected components of precipitation from the uppermost soil layers (grass, crops, etc.). The separately during these two events. We compare a sub- lack of dynamic vegetation could also play a role in set of CFSv2 forecasts against the analysis data within continuing transpiration even in dry conditions be- the context of the two drought events to see how well cause of the use of a vegetation phenology-based cli- the forecasts captured different drought characteristics. matology in the model. Noah-MP includes a simple Drought generally involves negative anomalies in sev- module for representing dynamic vegetation, which eral hydrometeorological variables as the signal travels inprinciplecouldhelptoimprovethesimulationof through the hydrologic cycle. Therefore, in order to evapotranspiration. However, this potential improve- study the drought characteristics, we looked at four ment is not straightforward. For example, a version of different variables, namely, precipitation, evapotrans- Noah-MP with dynamic vegetation still produced more piration, precipitable water, and soil moisture, for the evapotranspiration over most of the CONUS com- corresponding drought years, as well as one preceding pared to the baseline model Noah (Yang et al. 2011). and one following year. Soil moisture was underestimated in Texas, whereas CFSv2 analysis was an appropriate choice as the in the Upper Midwest, the first half of summer showed reference dataset since it compared well with datasets overestimation and the other half showed underesti- from other sources, both for spatial and temporal mation. The underestimation of soil moisture in Texas precipitation fields. The drought was evident in the 152 JOURNAL OF HYDROMETEOROLOGY VOLUME 20 anomalously low values of all four variables in both contribute to the misrepresentation of advection in the regions, while the strongest signals were seen in pre- atmospheric component of the modeling system. cipitation and evapotranspiration. The Texas drought Clearly, it is not straightforward to pinpoint the exact was dominated by the changes in moisture transport, solutions to the existing problems within the model- where the internal atmospheric variability also played ing system without a comprehensive set of controlled acrucialrole(Seager et al. 2014). In the Upper experiments. This is an interesting research area where Midwest, a lack of precipitation-generating mecha- more work is needed. The models need to be tested nisms contributed to the drought event, as also sug- rigorously to select the best combination of the param- gested by Hoerling et al. (2014). Drought in the Upper eterizations for the problem in hand. Within a given Midwest was short, emerging in May/June of 2012, coupled model, the land surface component needs to be whereas in Texas, it started as early as April of 2011. improved taking into account the inconsistencies that June and August appeared to be the driest months in come therein (Liu et al. 2017; Dirmeyer et al. 2018). A Texas, while in the Upper Midwest, July and August multimodel assessment can better address the issue of were the driest, and in both cases, drought was allevi- model structural uncertainty. Uncertainties arising from ated by the end of August. The significant sources of the choice of the input variables and initial and bound- moisture in Texas are the tropical and midlatitude ary conditions also need to be taken into account. In our Atlantic, recycling, and the CONUS, whereas, in case, although the precipitation fields from different the Upper Midwest, the midlatitude Pacific, tropical products matched quite well (Fig. 2), there could still be Atlantic, CONUS, and recycling are the substantial discrepancies in other variables. More research will also contributors. The Texas drought was caused primarily be needed to extend the forecast comparison to a more because of the reduced advection from oceanic sources detailed investigation of forecasting skill measured by as well as reduced recycling, while in the Upper Mid- different metrics at different initialization and lead west, several land regions, especially in the western times. Although many of these additional analyses were United States, also influenced the drought along with beyond the scope of this particular study, we hope that at the oceanic sources. The contributions from all the the very least, our study provides some useful insights on sources were low during the corresponding drought the existing problems by looking at droughts from the years. In both regions, partial shutdown of long-range perspective of atmospheric water partitioning by pre- moisture transport was evident, and in such conditions, cipitation recycling and sheds some light on the poten- land sources can contribute to alleviating drought in- tial solutions. tensity, even with negative anomalies. It should be noted that the recycling component can increase during Acknowledgments. Financial support for this research dry conditions, indicating that low precipitation is was provided by the NOAA Grant NA14OAR4310236 mostly due to decreased advection, which increases (Understanding the Role of Land-Atmospheric Cou- recycling (Dominguez and Kumar 2008). However, for pling in Drought Forecast Skill for the 2011 and 2012 US severe droughts, this negative recycling feedback ef- Droughts). Alexis Berg was supported by NOAA Grant fectively shuts down, and therefore, local evapotrans- NA15OAR4310091. Rongqian Yang from NCEP hel- piration ceases to contribute to precipitation. ped in transferring the CFSv2 forecasts data to Princeton We show that CFSv2 forecasts failed to capture dif- server. MSWEP precipitation data were obtained from ferent aspects of the drought events considered, espe- Hylke Beck, and Ming Pan provided the NLDAS2 cially in the Upper Midwest. The ensemble mean precipitation data. Other members of the Terrestrial precipitation from the forecasts was close to the anal- Hydrology Research Group at Princeton University ysis for Texas, while significant overestimation was provided valuable feedback throughout the course of evident in the case of the Upper Midwest. Both recy- this research. CFS datasets are available from NOAA/ cled and advected components of precipitation were NCDC. The authors express no conflict of interest. overestimated. For evapotranspiration as well, the forecasts showed large overestimation. We argue that the lack of detailed process representations in the Noah REFERENCES land surface model within the CFSv2 system might Agudelo, J., P. A. Arias, S. C. Vieira, and J. A. Martínez, 2018: have played a key role in causing the poor performance Influence of longer dry seasons in the Southern Amazon of the forecasts. Thus, more detailed simulations of the on patterns of water vapor transport over northern South America and the Caribbean. Climate Dyn., https://doi.org/ terrestrial processes can potentially improve the CFS 10.1007/s00382-018-4285-1, in press. forecasts. The errors in the forecasting of atmospheric Anyah, R. O., C. P. Weaver, G. Miguez-Macho, Y. Fan, and circulation patterns several weeks in advance can A. Robock, 2008: Incorporating water table dynamics in JANUARY 2019 R O Y E T A L . 153

climate modeling: 3. Simulated groundwater influence on ——, ——, X. Z. Liang, and M. Ting, 2006: Impact of atmospheric coupled land-atmosphere variability. J. Geophys. Res., 113, moisture storage on precipitation recycling. J. Climate, 19, D07103, https://doi.org/10.1029/2007JD009087. 1513–1530, https://doi.org/10.1175/JCLI3691.1. Beck, H. E., A. I. J. M. Van Dijk, V. Levizzani, J. Schellekens, ——, ——, and E. R. Vivoni, 2008: Precipitation recycling vari- D. G. Miralles, B. Martens, and A. De Roo, 2017: ability and ecoclimatological stability—A study using NARR MSWEP: 3-hourly 0.258 global gridded precipitation (1979– data. Part II: North American monsoon region. J. Climate, 21, 2015) by merging gauge, satellite, and reanalysis data. Hy- 5187–5203, https://doi.org/10.1175/2008JCLI1760.1. drol. Earth Syst. Sci., 21, 589–615, https://doi.org/10.5194/ ——, J. C. Villegas, and D. D. Breshears, 2009: Spatial extent of the hess-21-589-2017. North American Monsoon: Increased cross-regional linkages ——, E. F. Wood, M. Pan, C. K. Fisher, D. G. Miralles, A. I. J. M. via atmospheric pathways. Geophys. Res. Lett., 36, L07401, van Dijk, T. R. McVicar, and R. F. Adler, 2019: MSWEP V2 https://doi.org/10.1029/2008GL037012. global 3-hourly 0.18 precipitation: Methodology and quanti- Findell, K. L., and E. A. B. Eltahir, 2003: Atmospheric controls on tative assessment. Bull. Amer. Meteor. Soc., https://doi.org/ soil moisture–boundary layer interactions. Part I: Framework 10.1175/BAMS-D-17-0138.1, in press. development. J. Hydrometeor., 4, 552–569, https://doi.org/ Bisselink, B., and J. Dolman, 2008: Precipitation recycling: Moisture 10.1175/1525-7541(2003)004,0552:ACOSML.2.0.CO;2. sources over Europe using ERA-40 data. J. Hydrometeor., 9, Gimeno, L., and Coauthors, 2012: Oceanic and terrestrial sources 1073–1083, https://doi.org/10.1175/2008JHM962.1. of continental precipitation. Rev. Geophys., 50,RG4003, Bo, Z., S. Islam, and E. A. B. Eltahir, 1994: Aggregation- https://doi.org/10.1029/2012RG000389. disaggregation properties. Water Resour. Res., 30, 3423– Goessling, H. F., and C. H. Reick, 2013: On the ‘‘well-mixed’’ as- 3435, https://doi.org/10.1029/94WR02026. sumption and numerical 2-D tracing of atmospheric moisture. Boyer, J. S., and Coauthors, 2013: The U.S. drought of 2012 in Atmos. Chem. Phys., 13, 5567–5585, https://doi.org/10.5194/ perspective: A call to action. Global Food Secur., 2, 139–143, acp-13-5567-2013. https://doi.org/10.1016/j.gfs.2013.08.002. Grigg, N. S., 2014: The 2011–2012 drought in the United States: Brubaker, K. L., D. Entekhabi, and P. S. Eagleson, 1993: New lessons from a record event. Int. J. Water Resour. Dev., Estimation of continental precipitation recycling. J. Climate, 6, 30, 183–199, https://doi.org/10.1080/07900627.2013.847710. 1077–1089, https://doi.org/10.1175/1520-0442(1993)006,1077: Herrera-Estrada, J. E., 2017: Advances in understanding the causes EOCPR.2.0.CO;2. and impacts of droughts in North America under current and Budyko, M. I., 1974: Climate and Life. D. H. Miller, Ed., In- future climates. Ph.D. dissertation, Princeton University, 169 ternational Geophysics Series, Vol. 18, Academic Press, pp., https://pqdtopen.proquest.com/pubnum/10637641.html. 508 pp. ——, Y. Satoh, and J. Sheffield, 2017: Spatiotemporal dynamics of Burde, G. I., and A. Zangvil, 2001: The estimation of regional global drought. Geophys. Res. Lett., 44, 2254–2263, https:// precipitation recycling. Part I: Review of recycling models. doi.org/10.1002/2016GL071768. J. Climate, 14, 2497–2508, https://doi.org/10.1175/1520-0442 Hoerling, M., J. Eischeid, A. Kumar, R. Leung, A. Mariotti, K. Mo, (2001)014,2497:TEORPR.2.0.CO;2. S. Schubert, and R. Seager, 2014: Causes and predictability of Daly, C., R. P. Neilson, and D. L. Phillips, 1994: A statistical- the 2012 great plains drought. Bull. Amer. Meteor. Soc., 95, topographic model for climatological precipitation over moun- 269–282, https://doi.org/10.1175/BAMS-D-13-00055.1. tainous terrain. J. Appl. Meteor., 33, 140–158, https://doi.org/ Hua, L., L. Zhong, and Z. Ke, 2016: Precipitation recycling and 10.1175/1520-0450(1994)033,0140:ASTMFM.2.0.CO;2. soil-precipitation interaction across the arid and semi-arid re- DeAngelis, A., F. Dominguez, Y. Fan, A. Robock, M. D. Kustu, gions of China. Int. J. Climatol., 36, 3708–3722, https://doi.org/ and D. Robinson, 2010: Evidence of enhanced precipitation 10.1002/joc.4586. due to irrigation over the Great Plains of the United States. ——, ——, and ——, 2017: Characteristics of the precipitation J. Geophys. Res., 115, D15115, https://doi.org/10.1029/ recycling ratio and its relationship with regional precipitation 2010JD013892. in China. Theor. Appl. Climatol., 127, 513–531, https://doi.org/ Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: 10.1007/s00704-015-1645-1. Configuration and performance of the data assimilation sys- Jiang, X., G. Y. Niu, and Z. L. Yang, 2009: Impacts of vegetation tem. Quart. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/ and groundwater dynamics on warm season precipitation over 10.1002/qj.828. the Central United States. J. Geophys. Res., 114, D06109, Dirmeyer, P. A., and K. L. Brubaker, 2007: Characterization of https://doi.org/10.1029/2008JD010756. the global hydrologic cycle from a back-trajectory analysis of Kam, J., J. Sheffield, and E. F. Wood, 2014: A multiscale anal- atmospheric water vapor. J. Hydrometeor., 8, 20–37, https:// ysis of drought and pluvial mechanisms for the Southeast- doi.org/10.1175/JHM557.1. ernUnitedStates.J. Geophys. Res. Atmos., 119, 7348–7367. ——, and Coauthors, 2016: Confronting weather and climate https://doi.org/10.1002/2014JD021453. models with observational data from soil moisture networks Karl, T. R., and Coauthors, 2012: U.S. temperature and drought: over the United States. J. Hydrometeor., 17, 1049–1067, Recent anomalies and trends. Eos, Trans. Amer. Geophys. https://doi.org/10.1175/JHM-D-15-0196.1. Union, 93, 473–496, https://doi.org/10.1029/2012EO470001. ——, and Coauthors, 2018: Verification of land–atmosphere Kobayashi, S., and Coauthors, 2015: The JRA-55 Reanalysis: coupling in forecast models, reanalyses, and land surface General specifications and basic characteristics. J. Me- models using flux site observations. J. Hydrometeor., 19, teor. Soc. Japan Ser. II, 93, 5–48, https://doi.org/10.2151/ 375–392, https://doi.org/10.1175/JHM-D-17-0152.1. jmsj.2015-001. Dominguez, F., and P. Kumar, 2008: Precipitation recycling vari- Koster, R. D., Z. Guo, R. Yang, P. A. Dirmeyer, K. Mitchell, and ability and ecoclimatological stability—A study using NARR M. J. Puma, 2009: On the nature of soil moisture in land sur- Data. Part I: Central U.S. plains ecoregion. J. Climate, 21, face models. J. Climate, 22, 4322–4335, https://doi.org/10.1175/ 5165–5186, https://doi.org/10.1175/2008JCLI1756.1. 2009JCLI2832.1. 154 JOURNAL OF HYDROMETEOROLOGY VOLUME 20

Liu, C., and Coauthors, 2017: Continental-scale convection- J. Hydrol., 167, 57–78, https://doi.org/10.1016/0022-1694(94) permitting modeling of the current and future climate of 02632-L. North America. Climate Dyn., 49, 71–95, https://doi.org/ Schär, C., D. Lüthi, U. Beyerle, and E. Heise, 1999: The soil– 10.1007/s00382-016-3327-9. precipitation feedback: A process study with a regional cli- Mallya, G., L. Zhao, X. C. Song, D. Niyogi, and R. S. mate model. J. Climate, 12, 722–741, https://doi.org/10.1175/ Govindaraju, 2013: 2012 Midwest Drought in the United 1520-0442(1999)012,0722:TSPFAP.2.0.CO;2. States. J. Hydrol. Eng., 18, 737–745, https://doi.org/10.1061/ Schubert, S. D., M. J. Suarez, P. J. Pegion, R. D. Koster, and (ASCE)HE.1943-5584.0000786. T. Bacmeister, 2004: On the cause of the 1930s Dust Bowl. Sci- Martinez, A. J., and F. Dominguez, 2014: Sources of atmospheric ence, 303, 1855–1859, https://doi.org/10.1126/science.1095048. moisture for the La Plata River Basin. J. Climate, 27, 6737– Seager, R., L. Goddard, J. Nakamura, N. Henderson, and D. E. 6753, https://doi.org/10.1175/JCLI-D-14-00022.1. Lee, 2014: Dynamical causes of the 2010/11 Texas–northern Mei, R., M. Ashfaq, D. Rastogi, L. R. Leung, and F. Dominguez, Mexico drought. J. Hydrometeor., 15, 39–68, https://doi.org/ 2015: Dominating controls for wetter South Asian summer 10.1175/JHM-D-13-024.1. monsoon in the twenty-first century. J. Climate, 28, 3400–3419, Trenberth, K. E., 1999: Atmospheric moisture recycling: Role of advec- https://doi.org/10.1175/JCLI-D-14-00355.1. tion and local evaporation. J. Climate, 12, 1368–1381, https://doi.org/ Mitchell, K. E., 2004: The multi-institution North American Land 10.1175/1520-0442(1999)012,1368:AMRROA.2.0.CO;2. Data Assimilation System (NLDAS): Utilizing multiple GCIP Van Der Ent, R. J., and H. H. G. Savenije, 2013: Oceanic sources products and partners in a continental distributed hydrological of continental precipitation and the correlation with sea modeling system. J. Geophys. Res., 109, D07S90, https://doi.org/ surface temperature. Water Resour. Res., 49, 3993–4004, 10.1029/2003JD003823. https://doi.org/10.1002/wrcr.20296. Nielson-Gammon, J. W., 2012: The 2011 Texas drought. Tex. Water Wang, H., R. Fu, A. Kumar, and W. Li, 2010: Intensification of J., 3 (1), 59–95. summer rainfall variability in the southeastern United States Pathak, A., S. Ghosh, J. A. Martinez, F. Dominguez, and P. Kumar, during recent decades. J. Hydrometeor., 11, 1007–1018, https:// 2017: Role of oceanic and land moisture sources and transport doi.org/10.1175/2010JHM1229.1. in the seasonal and interannual variability of summer monsoon Wimmers, A. J., and C. S. Velden, 2011: Seamless advective in India. J. Climate, 30, 1839–1859, https://doi.org/10.1175/JCLI- blending of total precipitable water retrievals from polar- D-16-0156.1. orbiting satellites. J. Appl. Meteor. Climatol., 50, 1024–1036, Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s https://doi.org/10.1175/2010JAMC2589.1. Modern-Era Retrospective Analysis for Research and Ap- Yang, Z.-L., and Coauthors, 2011: The community Noah land plications. J. Climate, 24, 3624–3648, https://doi.org/10.1175/ surface model with multiparameterization options (Noah- JCLI-D-11-00015.1. MP): 2. Evaluation over global river basins. J. Geophys. Res., Roundy, J. K., C. R. Ferguson, and E. F. Wood, 2013: Temporal 116, D12110, https://doi.org/10.1029/2010JD015140. variability of land–atmosphere coupling and its implications Zangvil, A., D. H. Portis, and P. J. Lamb, 2004: Investigation of for drought over the southeast United States. J. Hydrometeor., the large-scale atmospheric moisture field over the mid- 14, 622–635, https://doi.org/10.1175/JHM-D-12-090.1. westernUnitedStatesinrelationtosummerprecipitation. Saha, S., and Coauthors, 2006: The NCEP Climate Forecast Part II: Recycling of local evapotranspiration and associa- System. J. Climate, 19, 3483–3517, https://doi.org/10.1175/ tion with soil moisture and crop yields. J. Climate, JCLI3812.1. 17, 3283–3301, https://doi.org/10.1175/1520-0442(2004)017,3283: ——, and Coauthors, 2014: The NCEP Climate Forecast System IOTLAM.2.0.CO;2. version 2. J. Climate, 27, 2185–2208, https://doi.org/10.1175/ Ziese, M., and Coauthors, 2014: The GPCC Drought Index – A JCLI-D-12-00823.1. new, combined and gridded global drought index. Earth Savenije, H. H. G., 1995: New definitions for moisture recycling Syst.Sci.Data, 6, 285–295, https://doi.org/10.5194/essd-6- and the relationship with land-use changes in the Sahel. 285-2014.