1. Introduction
Perhaps the most striking feature in a satellite loop of the earth is that the atmosphere transports water across great distances. While this transport appears to occur more frequently in certain regions, it is not steady; rather, it is characterized by numerous transient features of many scales. Thus, to understand atmospheric moisture transport, including the dual role it plays in the global energy cycle and as the source of water over the continents, there is a need to understand how atmospheric variability on different time scales acts to transport moisture and, in turn, is affected by it (e.g., Schneider et al. 2010; Trenberth 2011). This is true not only for variations in moisture transport, including extreme precipitation events, but also for the impact of atmospheric variability on the climatological mean moisture transport that is the subject of this paper.
It is well known that transient eddies are a critical part of poleward mean moisture transport in the extratropics (e.g., Peixoto and Oort 1992). More recently, it has been suggested that virtually all extratropical moisture transport is focused within long, relatively narrow bands sometimes called “atmospheric rivers” (ARs; Zhu and Newell 1998, hereafter ZN; Ralph et al. 2004; Neiman et al. 2008b, hereafter N08). ZN suggested that moisture transport is predominantly confined to these ARs, so that at any given time and at any given latitude about 90% of the meridional moisture transport occurs within only 10% of the zonal band. ARs are particularly striking in column-integrated water vapor (IWV), such as is measured by the Special Sensor Microwave Imager (SSM/I), and at times they extend from deep in the tropics to midlatitudes (Ralph et al. 2011). The extent to which such ARs represent the transport of moisture from the tropics to the extratropics has been a matter of some debate (e.g., Bao et al. 2006; Knippertz and Wernli 2010), although recent research aircraft observations have confirmed that transport from the tropics can occur (Ralph et al. 2011). Studies have shown that the IWV bands generally are regions of strong surface convergence, and that their leading edges typically correspond to the strong moist low-level jet sometime called the “moist conveyor belt” associated with fronts (Bao et al. 2006; Knippertz and Wernli 2010). Additionally, observational case studies (Ralph et al. 2004, 2005, 2011; Neiman et al. 2008a), composites of many aircraft-observed events (Ralph et al. 2005), and statistical comparison of 8 yr of reanalysis against SSM/I observations (Ralph et al. 2006; N08) show that the IWV bands can correspond to regions of pronounced moisture flux, that is, atmospheric rivers.
Two complementary approaches have been used to investigate moisture transport. Given the episodic nature of the IWV bands, it seems natural to use a Lagrangian framework (e.g., Stohl and James 2005; Bao et al. 2006; Eckhardt et al. 2004; Dirmeyer and Brubaker 2007; Knippertz and Wernli 2010; Gimeno et al. 2010; Drumond et al. 2011) and follow the trajectories of individual moist air masses, either forward from many different starting locations to determine where the moisture ultimately goes or backward starting from specified locations and/or precipitation events to find relevant sources. For climate studies the analysis can be computationally expensive, and the trajectory model can be sensitive to errors in the input atmospheric fields as well as errors in the parameterizations and represented dynamics. Also, water vapor is not entirely a passive tracer, so the types of trajectories that can be considered are either limited, especially in their duration, or some assumption must be made to keep track of water phase changes.
A few past studies have divided atmospheric moisture transport into contributions from the transient, zonal mean, and stationary wave portions of the circulation (e.g., Peixoto and Oort 1992; Shaw and Pauluis 2012). However, most recent studies of variability on “low frequency” (LF) time scales (e.g., intraseasonal to interannual) typically define anomalies as departures from the time-averaged atmospheric state, since storm-track and climate dynamics in the troposphere are both strongly influenced by zonal and meridional asymmetries of the basic state (e.g., Blackmon et al. 1977; Simmons et al. 1983; Borges and Sardeshmukh 1995; Whitaker and Sardeshmukh 1998; Winkler et al. 2001; Chang et al. 2002; and many others). In this study, we investigate the separate contributions of synoptic and low-frequency anomalies, defined as time-varying departures from the seasonally varying basic state and split into high-frequency (periods < 10 days) and low-frequency (periods > 10 days) components, by determining their relative importance in the seasonally varying climatological mean moisture budget determined from (1). This approach is laid out in section 2 along with a description of the 40-yr-long dataset. Results are in section 3, where we find that despite the dominance of moisture transport by the mean circulation over the oceans, synoptic and LF time scales play critical roles in both meridional and ocean-to-land time-mean moisture transport. The contribution of atmospheric rivers to moisture transport is assessed in section 4, and a closer focus on extratropical LF moisture transport is in section 5. Concluding remarks are in section 6.
2. Data and analysis
In sigma coordinates, the vertically integrated moisture flux in (1) is 
The moisture budget was computed for 40 yr of National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis data covering the period 1968–2007, with wind fields adjusted toward momentum and mass balance using the improved iterative solution of the “χ problem” (Sardeshmukh 1993) discussed in the appendix. All moisture budget calculations were repeated using NCEP–Department of Energy (DOE) Global Reanalysis 2 (NCEP-2) data for 1979–2005; for the common period, the results have only minor quantitative differences, so for brevity they are not displayed here. After their calculation, all moisture flux divergence fields were smoothed for display purposes using the Sardeshmukh and Hoskins (1984, hereafter SH) spatial filter with n = 42 and r = 2.
3. Seasonal variation of the atmospheric moisture budget
a. Winter and summer global moisture transport
Figures 1 and 2 show the results of (4) for December–February (DJF) and June–August (JJA) 1968–2007. Each figure shows the moisture transport terms 
Terms in the mean atmospheric water budget (2) for DJF of 1968–2007. Vertically integrated moisture flux divergence (shading) and vertically integrated moisture flux (vectors) for (a) total, (b) mean, (c) LF, and (d) synoptic terms from (4). Moisture flux vectors are scaled by (top) 300 and (bottom) 30 kg m−1 s−1; the moisture flux divergence contour level, however, is the same in all four panels. For display purposes, moisture flux divergences are smoothed with the SH filter (see text).
Citation: Journal of Climate 25, 21; 10.1175/JCLI-D-11-00665.1
As in Fig. 1, but for JJA of 1968–2007.
Citation: Journal of Climate 25, 21; 10.1175/JCLI-D-11-00665.1
The transport by the mean 
Figures 1 and 2 might give a somewhat misleading picture of the water cycle because a large fraction of the atmospheric moisture transport essentially moves water zonally from one part of the ocean to another (which does have important implications for ocean dynamics by changing surface salinity; see, e.g., Huang 1993; Delcroix et al. 1996). While this “ocean to ocean” moisture transport is dominated by 
b. Ocean-to-land moisture transport
The importance of 
(left) Seasonal cycle of moisture transport from ocean to land in the (a) tropics, (b) Northern Hemisphere extratropics, and (c) Southern Hemisphere extratropics, where the tropical–extratropical boundaries are set as 20°N and 20°S. (right) Seasonal cycle of moisture transport into the specified regions of (d) North America, (e) Europe (where the eastern boundary is set as 60°E), and (f) the Arctic (defined as the region north of 70°N). The last pair of bars in each panel shows the annual mean terms. Mean moisture flux (transport) driven by the mean, synoptic, and LF anomalies into each region is determined by the areal average of the vertically integrated moisture flux convergence over each region. Mean precipitable water (
Citation: Journal of Climate 25, 21; 10.1175/JCLI-D-11-00665.1
During DJF, the pattern of 
c. Meridional moisture transport
The well-known importance of transients in driving mean meridional moisture transport (e.g., Peixoto and Oort 1992) is evident in Fig. 4, which shows the zonal average of each term contributing to 
Seasonal cycle of terms contributing to vertically integrated, zonally averaged mean meridional moisture flux 
Citation: Journal of Climate 25, 21; 10.1175/JCLI-D-11-00665.1
Moisture transport into the polar regions can be determined by the value of the zonally averaged meridional transport at 70°N and 70°S in Fig. 4. Mean transport into the Arctic (Fig. 3f) peaks during summer, in agreement with earlier studies (e.g., Serreze et al. 2006, 2007). LF anomalies drive close to two-thirds of this transport every month of the year. In the winter the LF transport occurs primarily over the Atlantic, but in the summer it is dominated by poleward transport from the large landmasses toward the Arctic Ocean (cf. Figs. 1c and 2c). Much of this transport occurs from Eurasia, consistent with an increased frequency of blocking there during summer (e.g., Tyrlis and Hoskins 2008; Dole et al. 2011) as well as a pronounced summertime maximum over northern Eurasia in 
d. Analysis of transport terms
It is instructive to separate moisture flux divergence 
Contributors to moisture flux divergence for DJF of 1968–2007: (left) divergence terms 
Citation: Journal of Climate 25, 21; 10.1175/JCLI-D-11-00665.1
The time-scale dependence of LF transport is examined by applying additional time filters. As in section 2, we define 
Moisture transport by bandpass anomalies for DJF of 1968–2007: vertically integrated moisture flux divergence (shading) and corresponding moisture fluxes (vectors). Note that the moisture flux divergence contour level is reduced (relative to Fig. 1) to 0.125 mm day−1 and the moisture flux vectors are scaled by 10 kg m−1 s−1. For display purposes, moisture flux divergences are smoothed with the SH filter (see text).
Citation: Journal of Climate 25, 21; 10.1175/JCLI-D-11-00665.1
4. Impact of “atmospheric rivers” on the atmospheric moisture transport
The statistical analysis described in the preceding section, of course, yields a picture that is representative of the net effect of all individual events. In this section we examine this more closely, also considering the impact of atmospheric rivers as described, for example, by ZN and N08. As an example, Fig. 7 shows a time–longitude diagram of the 4-times-daily Q and 
Hovmöller of 4-times-daily total vertically integrated moisture flux Q (vectors, scaled by 600 kg m−1 s−1) and vertically IWV 
Citation: Journal of Climate 25, 21; 10.1175/JCLI-D-11-00665.1
As in Fig. 7a, but for Q and 
Citation: Journal of Climate 25, 21; 10.1175/JCLI-D-11-00665.1
Because at any given time much of the transport is located within fairly narrow spatial bands, ZN suggested that the bands represent atmospheric rivers, which they defined as filament-like structures of moisture flux representing most of the global total moisture transport. They categorized these regions by finding all locations where the magnitude of Q, |Q|, was relatively higher than its zonal mean value. Specifically, their algorithm determined that a river existed wherever and whenever |Q| ≥ |Qmean| + 0.3(|Qmax| − |Qmean|), where Qmean is the zonal mean Q and Qmax is the longitudinal maximum, both of which are functions of latitude and time.
To gain a comprehensive picture of the effect of ARs on the atmospheric moisture transport, we composited Q in those regions and times in our 40-yr dataset where AR conditions occur, using the ZN definition, and also determined the frequency of AR condition occurrence worldwide. The results (Fig. 9a) confirm that, as ZN suggested from much more limited data, the flux associated with atmospheric rivers defined in this way represents a large portion of the total moisture flux field, and virtually all of the extratropical meridional transport (not shown). However, a comparison of Fig. 9d to Fig. 1a shows that the AR composite takes into account neither transport by the mean subtropical highs in the Southern Hemisphere (and similarly for the Northern Hemisphere during summer; not shown) nor the substantial zonal transport that remains in the extratropical jets of both hemispheres.
DJF 4-times-daily moisture flux composited by different AR criteria: (a) ZN criterion, (b) N08 criterion, and (c) “positive anomalous 
Citation: Journal of Climate 25, 21; 10.1175/JCLI-D-11-00665.1
One concern regarding the ZN definition is that it is somewhat ad hoc, since there is no precise justification for its form; in fact, ZN determined the threshold value 0.3 because it gave the “best” fit to the total moisture flux field computed from the data for one day, 12 October 1991. Changing the threshold parameter gives quite different results: if it is reduced (0.1), then almost all moisture flux worldwide is categorized as “AR” flux and in the northeast Atlantic, AR conditions occur more than 75% of the time; whereas if it is increased (0.5), then the frequency of AR events is so reduced that the AR composite explains only about half of the total flux in the North Pacific. Additionally, the ZN definition does not differentiate between transient and steady moisture transport. The mere fact that moisture transport is much stronger over the oceans than over land, as is the case for transport by the mean circulation in the extratropics (cf. Fig. 1b), is enough to cause many regions to nearly continuously reach the AR threshold, most obviously in the North Atlantic. In fact, all the regions in Fig. 9a where the AR conditions occur at least 20% of the time are also regions where the transport by the mean alone passes ZN ’s AR test. This sensitivity to an arbitrary parameter complicates any diagnosis of how ARs contribute to the total moisture transport.
Note from Fig. 7 that the moisture flux is typically strongest in regions where moisture anomalies are large. This relationship between moisture and moisture flux is fairly general in the extratropics: poleward of about 30°, the correlation between 
An important aspect of the above-mentioned criteria is that the AR region is defined as relatively narrow, which introduces an element of subjectivity; namely, how narrow is narrow enough? Also, as noted by Bao et al. (2006), ARs are generally coincident with strong surface convergence, so when narrowness is associated with frontal dynamics, it may not be a necessary condition. Nevertheless, these definitions capture the essence of extratropical moisture transport as is seen in Fig. 7, since they identify plumes of moisture with regions of intense poleward moisture transport, as in Ralph et al. (2004). This leads us to categorize AR conditions as the occurrence of episodic poleward-moving moisture plumes, without requiring a shape requirement. Figure 9c shows the results so obtained, by compositing over all times/locations for which the 4-times-daily (unfiltered) 
The composite in Fig. 9c shows that the extratropical moisture transport is associated primarily with the anomalous poleward advection of positive moisture anomalies. At any given time, then, ARs are indeed those regions where most of the extratropical moisture flux is located. It is also interesting that the variances of both moisture and meridional wind synoptic anomalies (shown for wintertime in Fig. 10) lie within the region of strongest climatological meridional moisture gradient, with mean moisture relatively well mixed both to the north and south (see the top panel of Fig. 10). Since the synoptic moisture transport is predominantly meridional, the AR composite suggests a simple “lateral mixing” argument for the moisture flux (illustrated in Fig. 11a): anomalous poleward wind generates a positive moisture anomaly (a “plume”) that transports moisture poleward, but at the same latitude anomalous equatorward wind does not generate a negative moisture anomaly, so it does not contribute to the transport. This is essentially the converse of the argument Pierrehumbert (1998) makes for the transport of dry extratropical air into the subtropics, so we have appropriated his term to describe the process (see also Pierrehumbert 2002; Caballero and Langen 2005; O ’Gorman and Schneider 2008). That is, a simple scale analysis for extratropical moisture transport is Q ≈ Qy ~ υΔ
Moisture and meridional wind DJF climatology. (top) Mean DJF climate of 
Citation: Journal of Climate 25, 21; 10.1175/JCLI-D-11-00665.1
Schematics of extratropical (a) synoptic transport and (b) LF transport. In (a), “lateral mixing” picture of synoptic moisture transport. At a given latitude indicated by the dashed line, a parcel advected by a poleward wind anomaly υ’ (red arrow) will be coming from the equatorward side, so it will have moisture 
Citation: Journal of Climate 25, 21; 10.1175/JCLI-D-11-00665.1
This picture can be somewhat extended in two ways. First, note that the maximum in 
5. LF transport over the wintertime extratropical oceans
The lateral mixing argument of the previous section does not appear to be consistent with the observed variability of LF moisture and circulation anomalies, also shown in Fig. 10, or their associated mean moisture transport. Most of the extratropical meridional wind variability on LF time scales, associated with changes in regional zonal jets and storm tracks, is located in the northeastern portions of the Atlantic and Pacific basins, away from regions of strong mean moisture gradient. Yet as seen in Fig. 5, LF moisture flux divergence is predominantly associated with the advection term. This suggests that a process connected to the typical large-scale LF anomalies of both the Pacific and Atlantic basins must be driving the extratropical LF transport. For example, over the Pacific a common LF anomaly involves a strengthening or weakening of the Aleutian low with corresponding wind anomalies, as illustrated in Fig. 11b. Changes in the surface zonal winds (red lines) will also change surface evaporation (Cayan 1992; Alexander and Scott 1997), and meridional wind anomalies will advect dry air anomalies equatorward and moist air anomalies poleward. This gives rise to LF moisture flux that is both northwestward and northeastward from the source region, with the anomalous moisture gradient in the same direction as the wind anomaly as in Fig. 11b. Note that a LF anomaly of either sign will lead to the same pattern of moisture transport, so on average this anomaly will contribute to mean transport.
To see if this effect exists in nature, we first computed the principal components (PCs) of 
Regression of 
Citation: Journal of Climate 25, 21; 10.1175/JCLI-D-11-00665.1
Previous studies (Cayan 1992; Alexander and Scott 1997) have identified the northeast Pacific and Atlantic as regions of strong latent heat flux exchange between the ocean and atmosphere on LF time scales, and this flux is strong enough to drive much of the North Pacific SST anomaly associated with ENSO (Alexander et al. 2002). The net energy flux associated with the North Pacific moisture source in Fig. 1c is L(E − P), which for L = 2.5 × 106 J kg−1 and E − P ~1.7 mm day−1 is ~50 W m−2. In comparison, the total 1968–2006 DJF mean latent heat flux in this location (35°N, 142°W) from the objectively analyzed air–sea fluxes (OAFlux) dataset (Yu et al. 2008) is about 85 W m−2. It is also interesting to note that a local ridge in the DJF mean latent heat flux field from the OAflux (not shown) is centered along a line that extends from about 22°N, 147°W to 46°N, 130°W, which is coincident with the maximum in 
6. Concluding remarks
Although there have been many previous analyses of the atmospheric moisture budget, including those that have demonstrated the importance of transient eddies to meridional moisture transport, it has not been previously shown how synoptic-versus-LF time scales impact climatological moisture transport. An analysis of the seasonal cycle of the mean vertically integrated atmospheric moisture budget using 40 yr of NCEP–NCAR reanalysis data reveals that during the cool season in the extratropics of both hemispheres, LF and synoptic anomalies play a significant role in the atmospheric transport of moisture from ocean to land. This occurs despite the fact that transport by the mean circulation generally has a much larger amplitude because much of the transport by the mean does not move moisture onto land so much as move moisture zonally from the western to the eastern margins of the ocean basins. In some regions, such as the North American southwest, Europe, and Australia, the LF transport is the largest contributor to net wintertime and even annual mean atmospheric moisture. The LF transport is also critical to the Arctic moisture budget throughout the year and reaches maximum amplitude during summer, associated with moisture transport from land to ocean, especially over Eurasia.
In addition, the differences between the LF and synoptic transport patterns are so striking as to suggest that LF transport does not merely represent a red-noise residual of synoptic variability, but that the dynamical processes driving LF transport are fundamentally different from those driving synoptic transport. Despite its relatively small impact in many regions of the globe, LF transport is a key moisture source for continental precipitation during winter. Note that while the sources associated with synoptic transport are fairly similar to the dominant global mean moisture sources, LF transport sources are not and, in fact, in many areas oppose the mean. This suggests that it may be of interest to consider these regions as starting points for Lagrangian analyses, especially for case studies of moisture source regions connected to LF variability.
This paper has also examined the potential role of atmospheric rivers in the global water budget and explored a method to diagnose systematically AR contributions to moisture transport without necessarily including a dependence on width and length (e.g., large values of IWV in long and narrow regions in the extratropics) used in recent diagnostic studies by Ralph et al. (2004, 2005, 2006, 2011) and Neiman et al. (2008a,b). The results verify that ARs are the primary regions where extratropical atmospheric moisture transport occurs. An individual AR event is the sum of its mean, synoptic, and LF components. AR moisture transport over the northern midlatitude oceans then essentially consists of poleward and eastward advection of a moisture plume originating within subtropical source regions, plus additional moisture extracted from the ocean in the western storm-track region by synoptic-scale meridional winds, plus moisture extracted in the northeast part of the basin depending on the state of the LF anomaly (e.g., an intensification of the Aleutian low), and minus the water precipitated out poleward of the storm track across the ocean. In this view, ARs do not simply represent trajectories of moisture transport from the tropics/subtropics, since on average ARs also pick up additional moisture as they cross the oceans, a point similar to the one made in case studies of some AR events by Bao et al. (2006).
It has been shown that on at least a few occasions, some moisture may be transported directly from the tropics within ARs (Bao et al. 2006; Stohl et al. 2008; Ralph et al. 2011). In fact, a moisture source for North America due to LF variability exists in the eastern tropical Pacific, although it may be more relevant for Mexico than regions farther north. But we also find a mechanism with perhaps greater impact that represents at most only an indirect effect of the tropics on extratropical moisture and its transport. During wintertime, ENSO is well known to cool (warm) the North Pacific sea surface by intensifying (weakening) the Aleutian low leading to enhanced (weakened) latent heat flux (the “atmospheric bridge”; Alexander et al. 2002). Our results suggest that during this process, moisture is extracted from the sea surface for LF anomalies of either sign, with much of this moisture transported toward western North America. That is, tropical forcing can produce circulation anomalies that transport additional moisture from an extratropical source while not actually transporting moisture all the way from the tropics. Note that extratropical LF variability of this type can also occur without tropical forcing (e.g., Winkler et al. 2001), and that tropical forcing details may influence the extratropical LF anomalies (e.g., Winkler et al. 2001; Di Lorenzo et al. 2010) and how they interact with the sea surface. Whether this process is likewise important to moisture transported by individual synoptic and LF events, including those ARs that give the appearance of direct transport of moisture from the tropics, is the subject of our current research.
Finally, we note that changes in the hydrological cycle are also fundamental to anthropogenic climate change scenarios, impacting precipitation patterns (e.g., Trenberth 2011), large-scale circulation (Held and Soden 2006), and driving much of the global “warming” itself (e.g., Solomon et al. 2007; Compo and Sardeshmukh 2009). For example, it has been suggested that in a global sense, specific humidity may increase with a Clausius–Clapeyron relationship to increasing surface temperatures but precipitation may not, so that atmospheric circulation must weaken to compensate (Held and Soden 2006). However, where LF and/or synoptic transports oppose transport by the mean, as is the case for some areas in the extratropics and even some tropical convergence zones, it is plausible that increased variability could also weaken precipitation. Also, anthropogenic impacts on the northeast Pacific and Atlantic basins that are key moisture sources for extratropical landmasses, and on the LF variability that drives the moisture transport into land, are uncertain. Thus, understanding potential anthropogenic changes in the earth ’s hydrological cycle likely requires understanding corresponding changes in atmospheric variability, especially on low-frequency time scales.
Acknowledgments
The authors wish to thank Jim Adams for drafting Fig. 11; Chris Winkler for his assistance in generating Fig. A1; Gary Wick for his assistance with the SSM/I data; and Alan Betts, Paul Dirmeyer, and Tapio Schneider for their useful comments.
APPENDIX
Dynamically Consistent Estimates of Wind: The Chi Problem
There are many potential sources of error in computing (1) from analyzed datasets, but perhaps the most important is the notable difference in the divergent wind field between reanalyses. This is particularly true in the tropics (Newman et al. 2000), but it can even be true in the extratropics, such as for the low-level jet (LLJ), which transports a significant fraction of moisture during summer (e.g., Helfand and Schubert 1995). Thus, the most important correction to Q can be made by improving the wind analysis [Trenberth and Guillemot 1995; Wang and Paegle 1996; Min and Schubert 1997; Mo and Higgins 1996; see also Bengtsson et al. (2004), who find only a minor impact of assimilation of humidity observations on the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) hydrological cycle]. Specific humidity corrections on the order of 3% have also been made, but these have typically been applied only to the mean field (Large and Yeager 2009). For daily averaged values, we find that for the years 1997–2007 over the oceans, 
How can we better estimate the wind field? One approach is to note that the error in the analyzed wind fields is predominantly in the divergent component of the wind and less so for the rotational component, consistent with the fact that the large-scale vorticity analyses produced at different data centers are in much better agreement than the corresponding divergence analyses (e.g., Newman et al. 2000). One way to correct the analyzed divergence is by constraining the winds to minimize imbalances in both the mass and vorticity budgets, thus enforcing dynamical consistency on the divergent circulation. This approach, known as the “chi problem” (Sardeshmukh 1993), has been successfully used to correct tropical divergence fields (Sardeshmukh and Liebmann 1993; Sardeshmukh et al. 1999), but the approach is applicable globally. A long-term global heating dataset developed using the “chi corrected” horizontal wind and vertical velocity fields, where heating is then estimated as a residual in the heat budget, has been used for studies of short-term climate variability in and related to the tropics (Winkler et al. 2001; Lin et al. 2004; Newman and Sardeshmukh 2008; Newman et al. 2009), since we have found that this technique yields improved diabatic heating estimates (Sardeshmukh et al. 1999). It can similarly improve moisture flux estimates. Fig. A1a shows that in the tropics, the chi-corrected vertical profile of the November–February 1992/93 mean “moisture sink” (Q2; Yanai et al. 1973) compares better with Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) observations (Johnson and Ciesielski 2000) than does the same quantity computed from the NCEP reanalysis. The chi correction also acts to slightly lower the low-level jet altitude over the central United States, as shown in Fig. A1b for July 1993. A similar change is evident during June–August 1994 (not shown), which is consistent with profiler data showing the jet centered about 50 mb lower than in the reanalysis data (Higgins et al. 1997). The chi correction is a conservative adjustment to wind, well within observational error. Yet, during the warm season, a slightly stronger and lower chi-corrected low-level jet results in a larger estimate of P − E in the Great Plains (Fig. A1c), consistent with earlier work suggesting a large dry bias in the reanalysis in the North American warm season (e.g., Mo and Higgins 1996; Yeh et al. 1998; Roads and Betts 2000; Lenters et al. 2000).
Comparison of chi-corrected and NCEP moisture flux divergences. (a) Vertical profile of seasonally averaged (1 Nov 1992–28 Feb 1993) moisture sink Q2 from observations (black line), chi-corrected fields (red line) and NCEP reanalysis (blue line). Units are K day−1. Observations are taken from Johnson and Ciesielski (2000) over the intensive flux array (IFA) of COARE. NCEP and chi-corrected fields are measured at an analysis grid point (1.4°S, 155°E) near the center of the IFA region. (b) Latitude-sigma cross sections of July 1993 monthly-mean 1200 UTC meridional wind speed, averaged between 95.5° and 98.5°W. (left) Chi-corrected total meridional wind. Contour interval is 1.2 m s−1; red shading indicates positive values and starts at 1.2 m s−1. (right) Difference fields (chi-corrected winds minus NCEP reanalysis winds). Contour interval is 0.4 m s−1; red shading indicates positive values and blue shading indicates negative values. Shading starts at +0.4 m s−1. Zero contour has been omitted for clarity. (c) Seasonal cycle of P − E, area averaged over a region similar to the Mississippi basin region defined by Roads et al. (1994).
Citation: Journal of Climate 25, 21; 10.1175/JCLI-D-11-00665.1
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