Water Vapor Transport and the Production of Precipitation in the Eastern Fertile Crescent

J. P. Evans Department of Geology and Geophysics, Yale University, New Haven, Connecticut

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R. B. Smith Department of Geology and Geophysics, Yale University, New Haven, Connecticut

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Abstract

The study presented here attempts to quantify the significance of southerly water vapor fluxes on precipitation occurring in the eastern Fertile Crescent region. The water vapor fluxes were investigated at high temporal and spatial resolution by using a Regional Climate Model [fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)–Noah land surface model] to downscale the NCEP–NCAR reanalysis. Using the Iterative Self-Organizing Data Analysis Techniques (ISODATA) clustering algorithm, the 200 largest precipitation events, occurring from 1990 through 1994, were grouped into classes based on the similarity of their water vapor fluxes. Results indicate that, while southerly fluxes were dominant in 24% of tested events, these events produced 43% of the total precipitation produced by the 200 largest events. Thus, while the majority of precipitation events occurring in the Fertile Crescent involve significant water vapor advected from the west, those events that included southerly fluxes produced much larger precipitation totals. This suggests that changes that affect these southerly fluxes more than the westerly fluxes (e.g., changes in the Indian monsoon, movement of the head of the Persian Gulf, etc.) may have a relatively strong affect on the total precipitation falling in the Fertile Crescent even though they affect relatively few precipitation events. To obtain a clearer view of the precipitation mechanisms, the authors used a linear model, along with the estimated water vapor fluxes, to downscale from 25 to 1 km. The result shows a spectrum of mountain scales not seen in the regional model, exerting tight control on the precipitation pattern.

Corresponding author address: Jason Evans, Department of Geology and Geophysics, Yale University, P.O. Box 208109, New Haven, CT 06520-8109. Email: jason.evans@yale.edu

Abstract

The study presented here attempts to quantify the significance of southerly water vapor fluxes on precipitation occurring in the eastern Fertile Crescent region. The water vapor fluxes were investigated at high temporal and spatial resolution by using a Regional Climate Model [fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)–Noah land surface model] to downscale the NCEP–NCAR reanalysis. Using the Iterative Self-Organizing Data Analysis Techniques (ISODATA) clustering algorithm, the 200 largest precipitation events, occurring from 1990 through 1994, were grouped into classes based on the similarity of their water vapor fluxes. Results indicate that, while southerly fluxes were dominant in 24% of tested events, these events produced 43% of the total precipitation produced by the 200 largest events. Thus, while the majority of precipitation events occurring in the Fertile Crescent involve significant water vapor advected from the west, those events that included southerly fluxes produced much larger precipitation totals. This suggests that changes that affect these southerly fluxes more than the westerly fluxes (e.g., changes in the Indian monsoon, movement of the head of the Persian Gulf, etc.) may have a relatively strong affect on the total precipitation falling in the Fertile Crescent even though they affect relatively few precipitation events. To obtain a clearer view of the precipitation mechanisms, the authors used a linear model, along with the estimated water vapor fluxes, to downscale from 25 to 1 km. The result shows a spectrum of mountain scales not seen in the regional model, exerting tight control on the precipitation pattern.

Corresponding author address: Jason Evans, Department of Geology and Geophysics, Yale University, P.O. Box 208109, New Haven, CT 06520-8109. Email: jason.evans@yale.edu

1. Introduction

The Fertile Crescent is defined here as an area encompassing southeast Turkey, northeastern Syria, northern Iraq, and northwestern Iran and is shown in Fig. 1 (it is the eastern half of the full Fertile Crescent). The area of interest covers approximately 20 000 km2. It is centered over a large precipitation maxima found in both data and model results and includes most of the headwaters of the Tigris River; hence precipitation here is an important source of freshwater for parts of Turkey, Syria, and Iraq. Being a dominantly arid area, relatively little precipitation recycling occurs over the land and the surrounding water bodies are major contributors to atmospheric water vapor. To the northwest is the Black Sea, to the northeast is the Caspian Sea, to the west is the Mediterranean, and to the south is the Persian Gulf. While it has been generally accepted that the area is dominated by storm systems that move in from the Mediterranean Sea, earlier modeling work (Evans et al. 2004) indicated that water vapor contributing to some of these storm events is dominated by a southerly flux. Quantifying the significance of this contribution provides some indication of Fertile Crescent precipitation sensitivity to changes in the condition of the Persian Gulf relative to the Mediterranean Sea. This may have important implications for human settlement during the Holocene when the Persian Gulf is known to have changed substantially (Aqrawi 2001; Lambeck 1996; Pournelle 2003).

The Fertile Crescent region contains the longest archeological record of human civilization. As such, Holocene climate changes and what relationship they may have with the history of human settlement in the region are of continuing interest. A number of global climate model studies into the climate at various times during the Holocene have been conducted (Braconnot et al. 2000; Coe and Bonan 1997; Kutzbach et al. 1996; Liu et al. 2003; Vettoretti et al. 1998). While these model studies use available paleoclimate data for verification, this data can be relatively sparse. One such form of paleoclimate data is stable isotope paleodata extracted from, among other things, deep groundwater. Recent studies have used stable isotopes of water in precipitation to identify the source regions of the water vapor (Weyhenmeyer et al. 2000; Yamanaka et al. 2002); this requires that the source regions be significantly different in terms of isotopic composition. For the Fertile Crescent the main source regions are the Mediterranean Sea and the Persian Gulf/Arabian Sea. Similar source regions were investigated in a study by Weyhenmeyer et al. (2000) that demonstrated substantial differences in the meteoric water line associated with each source. Hence this study sets out to quantify the present relative contributions of each source region to precipitation in the Fertile Crescent. Armed with this knowledge and a time series of stable isotopic data from paleogroundwater one could determine how the relative contribution of each source region has changed throughout the Holocene; that is, one could imply changes in the dominant storm systems and tracks through the region.

Observations of atmospheric water vapor at high temporal and spatial resolution do not currently exist; hence a high-resolution regional climate model (RCM) simulation was used to investigate the water vapor fluxes (section 2). Section 3 presents some observational evidence for the presence of moist air masses in the south that connect with the Fertile Crescent precipitation events and compares these with RCM results. The precipitation events are then clustered according to the water vapor fluxes into and out of the region (section 4) with the results of the study presented in sections 5. Section 6 investigates the small-scale structure and orographic nature of the precipitation using a high-resolution linear model. The conclusions of the study are presented in section 7.

2. Regional climate model (MM5-Noah)

The fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) is described in Dudhia (1993) and Grell et al. (1994). MM5 is a limited-area nonhydrostatic model that uses a terrain-following vertical coordinate system. It has two-way nesting capabilities and flexible physics options. In this study MM5 was implemented with the Reisner mixed-phase explicit moisture scheme (Reisner et al. 1998), the medium-range-forecast (MRF) planetary boundary layer scheme (Hong and Pan 1996), the rapid radiative transfer model (RRTM) radiation scheme (Mlawer et al. 1997), and the Grell scheme for convective precipitation (Grell et al. 1994).

MM5 is operationally linked with the Noah land surface model (LSM). Noah is a direct descendent of the Oregon State University (OSU) LSM (Mahrt and Ek 1984; Mahrt and Pan 1984; Pan and Mahrt 1987), a sophisticated land surface model that has been extensively validated in both coupled and uncoupled studies (Chen and Mitchell 1999; Chen and Dudhia 2001). The Noah LSM simulates soil moisture, soil temperature, skin temperature, snowpack depth and water equivalent, canopy water content, and the energy flux and water flux terms of the surface energy balance and surface water balance. In its MM5-coupled form Noah has a diurnally dependent Penman potential evaporation (Mahrt and Ek 1984), a four-layer soil model (Mahrt and Pan 1984), a primitive canopy model (Pan and Mahrt 1987), modestly complex canopy resistance (Jacquemin and Noilhan 1990), and a surface runoff scheme (Schaake et al. 1996).

MM5 has been applied successfully at grid-cell resolutions ranging from greater than 100 km to less than 1 km and is used for both weather forecasts and climate research (Zaitchik et al. 2005; Evans et al. 2005). Here we apply the model at 25-km horizontal resolution over a domain that includes much of the Middle East and the surrounding water bodies. Figure 1 shows the model domain excluding the rows and columns that are directly influenced by the boundary conditions. This domain was initialized for a simulation beginning in November 1989 with initial and boundary conditions obtained from the National Centers for Environmental Prediction (NCEP)–NCAR reanalysis. The first two months of model run are discarded, allowing fields such as soil moisture to “spin up,” and the following five years (1990–94) are simulated. The model was run with 23 vertical levels, which were spaced more tightly near the ground surface.

Previously an identical simulation was performed with the RegCM2 regional climate model, which was extensively evaluated against multiple datasets (Evans et al. 2004). This MM5 simulation was evaluated against the same datasets and found to be an improvement over the RegCM2 simulation in a number of ways. These include significant improvement in the sea surface temperatures of surrounding water bodies, reduction in seasonal temperature biases and annual temperature range, improved snow cover extent, better precipitation pattern correlation, and, in most places, better annual cycles of precipitation. As an example of this evaluation, Fig. 2 shows the observed and modeled monthly precipitation in the Fertile Crescent. The observations are taken from the Food and Agriculture Organization of the United Nations (FAOCLIM version 1.2). This dataset includes monthly values for standard climate quantities taken from ground stations, averaged over a period from 1940 to 1970. The NCEP–NCAR reanalysis significantly overestimates the spring precipitation for the area, while the MM5 simulation corrects this error and produces a precipitation cycle that is much closer to the observations.

3. Observations of atmospheric water vapor

Under a National Aeronautics and Space Administration (NASA) peer-reviewed contract, STC-METSAT has produced a multiyear total (1988–99) global water vapor dataset, named NVAP, an acronym for NASA Water Vapor Project. The total column (integrated) water vapor dataset comprises a combination of radiosonde observations, Television and Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS), and Special Sensor Microwave Imager (SSM/I) datasets. STC-METSAT developed methods to process the data at a daily time scale and 1° × 1° spatial resolution. Details of the method used can be found in Randel et al. (1996).

While not representing water vapor flux—that is, the movement of water vapor—but rather representing the presence of water vapor, this dataset provided the best opportunity to confirm a connection between moist air masses in the Fertile Crescent and farther to south. The corresponding total column-integrated water vapor simulated by the RCM was also calculated and compared with the NVAP dataset. Figure 3 presents MM5 and NVAP total column water vapor for 30 January 1992. This is a class 2 event (defined in the following sections) and demonstrates an example of a moist air mass reaching toward northern Iraq that is strongly connected to southern water bodies. While the MM5 simulation appears too moist compared to NVAP, a good correlation in the patterns is seen with a pixel-by-pixel pattern correlation of 0.87. Calculating the pattern correlation between NVAP and MM5 for the 200 largest precipitation events reveals a mean pattern correlation of 0.66, indicating reasonably good agreement between the RCM simulation and the NVAP dataset.

4. Precipitation event classification

To obtain a quantitative sense of the importance of this southerly water vapor flux for the production of precipitation in the Fertile Crescent the events are clustered based on the fluxes through the sides of the box shown in Fig. 1. Each event is represented by a data series consisting of a 3-hourly flux series from each direction (north, south, east, and west) and the precipitation series. These series extend from one day before until one day after the time of peak precipitation. The use of all of the major fluxes guarantees that the complete data series has a mean close to zero regardless of the size of event and removes the potential for the clustering algorithm to cluster points based on differences in their means.

This clustering is performed using the iterative clustering algorithm ISODATA (Ball and Hall 1967) applied to the above data series for the 200 largest precipitation events over the 5-yr period. These events produce a minimum of ∼1.66 mm of precipitation over 2 days. In total they account for ∼65% of all the precipitation falling within the box indicated in Fig. 1 over the 5-yr period.

The algorithm was run using statistical initialization starting with between 8 and 12 classes. In all cases only four classes of events, with a minimum of five members, were produced by the algorithm. That is, the algorithm found it necessary to merge classes due to their similarity until only four classes remained. This increases the confidence that this four-class clustering is a robust result.

As a statistical measure of the uncertainty associated with the allocation of an event to a class we introduce the measure U:
i1525-7541-7-6-1295-e1
where d*i is the Euclidean distance from the ith event to its assigned class mean, dki is the Euclidean distance from the ith event to class k, and c is the total number of classes. For this measure, with four classes, a value of 0.25 is achieved if a point lies exactly in between all four classes while, if the distance from a point to its nonassigned class means is 50% further then the distance from that point to its assigned class mean, a value of ⅓ is achieved.

5. Results

Examining the precipitation time series for the Fertile Crescent reveals no clear interannual trend. While a strong seasonal cycle exists, there is significant variation in which month provides the most precipitation each year. This seasonal cycle is seen clearly in Fig. 4, where none of the 500 largest daily precipitation events occur in July or August. The figure shows that for large events (top 10) there are spring maxima in March and May. This does not exist for the smaller events, which show a plateau maximum from November through February followed by a spring “shoulder” extending through May. There is also an autumn maximum in large events in November, which persists right through the top 200 events but is not apparent in the top 500. These spring and autumn maxima imply the combination of winter-type circulations with the added solar (convective) energy needed for the really big events. The amount of convective precipitation modeled is substantially smaller than the total precipitation, with the largest 10 events producing only 3.3 to 13 mm of convective precipitation compared to at least 14.5 mm of total precipitation. The timing of this convective precipitation displays April–May and November maxima. Thus it appears that modeled convection plays a role in the overall November maxima as well as the April–May shoulder.

The number of events, total precipitation, and mean uncertainty associated with each class produced by the ISODATA algorithm is given in Table 1. The uncertainty is the mean U [defined by Eq. (1)] for all events in that class. Class 2 is the most certain class with events being (on average) more than twice as far from the other class means as from its own class mean. Class 3 is the most uncertain class, but even these events tend to be ∼53% further from the other class means than their own class mean. Approximately three-quarters of all events produce U > ⅓, implying that the storms look similar to the canonical storm for that class. This leaves ∼one-quarter of the events with relatively uncertain class assignments.

The mean water vapor fluxes for each of the classes are presented in Fig. 5. In every case fluxes from the west and south tend to be into the box while fluxes from the east and north tend to be out of the box. The flux of water due to precipitation is considered to be out of the atmospheric box. Focusing on the incoming fluxes, the events are split according to the relative importance of westerly and southerly fluxes. Classes 1 and 3 are dominated by westerly fluxes, while class 4 is dominated by southerly fluxes. Class 2 has a dominant southerly flux just before reaching the precipitation peak; this flux then rapidly decreases and the system is again dominated by the westerly flux.

Events in class 1 tend to produce the least amount of precipitation overall. The magnitude of all the fluxes tends to increase with increasing class number, with events in class 4 producing the most precipitation. For all events, the peak in total incoming water flux occurs ∼6 h before the peak in precipitation.

Several quantitative conclusions can be drawn from the number and size of events in each class, as presented in Table 1. In every case the percentages given below refer only to the tested events, that is, the 200 largest precipitation events. Westerly fluxes play a dominant role in only 44.5% of events (classes 1 and 3), which account for only 35% of the precipitation, contrary to what was expected. Fluxes from the west and south contribute similar amounts in 31.5% of events (class 2), representing 22% of the total precipitation. Only 24% of the events are dominated by southerly fluxes; however, these events tend to be large and account for ∼43% of the total precipitation. That is, while events dominated by westerly fluxes are more common they produce less actual precipitation than events dominated by southerly fluxes.

This implies that storm systems that produce large southerly fluxes of water vapor, while less common, are very important in the cumulative precipitation total. The presence or absence of a few of these events may be the difference between an average and a poor precipitation year. While the data required to effectively evaluate this model prediction do not exist, we performed a low-resolution, first-order test by looking for correlations between southerly fluxes from the NCEP–NCAR reanalysis and precipitation from the Global Precipitation Climatology Project (GPCP: Huffman et al. 2001). Using daily data from the year 2000 it was found that increased southerly flux across the southern boundary tends to correlate with increased precipitation with a correlation coefficient of 0.52, which is statistically significant at the 0.99 level.

Characteristics of storm systems such as mean sea level (MSL) pressure, 500-hPa geopotential height, and potential vorticity provide insight into differences between the storm systems of different classes. These fields are averages of all events in that class at each time step relative to the time of peak precipitation. Figures 6 and 7 present snapshots of these fields at the time of peak precipitation for each class.

Investigating Fig. 6 reveals that class 1 MSL pressure is characterized by the presence of a high pressure center just south of the western Black Sea, another present in the southern Zagros Mountains/Iranian plateau, and a trough of low pressure extending from the Persian Gulf up to the Fertile Crescent. The 500-hPa geopotential height has a broad low north of the Caucasus and a shallow trough extending from the eastern Black Sea south into Syria. The midlevel tropospheric winds bend sharply around this trough. They reach the Mediterranean Sea from the northwest and pass through the Fertile Crescent heading toward the east-northeast. The low-level winds are weaker and pass through the Fertile Crescent heading east-southeast. The MSL pressure shows that a low pressure center moved in along the northern Mediterranean Sea before evolving into the low pressure trough seen in Fig. 6. Subsequently this trough retreats toward the Persian Gulf. Meanwhile, almost a day behind the low pressure center, the high pressure center moves into Turkey from the northwest. It drifts eastward, strengthening as the low pressure trough retreats. The MSL high in the southern Zagros Mountains remains fairly constant. The MSL low pressure center is associated with the movement of the midlevel trough that swings from west to east through the domain, weakening after the precipitation peak. The potential vorticity (shown in Fig. 7) moves in synchronization with the midlevel trough. It becomes more organized and strengthens from a day before until the peak precipitation after which it begins to dissipate. The peak of potential vorticity remains north of the Taurus Mountains throughout the event.

The MSL pressure of events in class 2 is quite similar to that of class 1 but with a deeper low pressure trough and a stronger high pressure center (Fig. 6). Significant differences are, however, present in the 500-hPa geopotential height field. The 500-hPa low center is north of the Caspian Sea. While this center is not as deep as that of class 1, the trough extends considerably farther south than the trough in class 1. The tropospheric winds bend sharply around the geopotential trough with winds on the western side of the Fertile Crescent region coming from the northwest while on the eastern side the winds are heading northeast. The surface winds are heading southeast through the Fertile Crescent region, providing significant vertical shear in the winds. While at the time of peak precipitation the MSL fields of class 1 and class 2 appear similar, they developed in different ways. In class 1 a low pressure center moved in along the northern Mediterranean Sea, while here the MSL low pressure trough over the lowland parts of the Middle East was created by the development of a low pressure center over north-central Saudi Arabia during the preceding day. The 500-hPa trough axis swings around from the Mediterranean Sea a day before peak precipitation to be almost directly south of the low center a day after the peak precipitation. A potential vorticity center develops over Turkey more than a day before the peak precipitation and moves eastward across the domain, with the midlevel trough.

Class 3 MSL pressure shows an Iranian plateau high and a low pressure center northwest of the top of the Persian Gulf. The majority of the region is dominated by low pressure with the deepest area extending from around the low pressure center up to the northeastern Mediterranean Sea. At the 500-hPa level a low center exists north of the Black Sea with a trough extending southward over Cyprus. Winds at the 500-hPa level in the Fertile Crescent region are from the southwest, while surface winds are almost westerly. The evolution of MSL pressure includes the relatively stable Iranian plateau high and a low pressure center that moves into the Eastern Mediterranean from the west. This center stalls near the coast of Syria, while the low pressure continues to extend southeastward. A secondary low northwest of the Persian Gulf develops and strengthens, becoming the primary low by the time of peak precipitation before moving off to the southeast. The 500-hPa trough is anchored to the north of the Black Sea and the trough axis swings from west to east into the Eastern Mediterranean Sea where it stalls and broadens. Prior to the peak precipitation the surface wind field contains a zone of convergence extending from the Zagros Mountains northward through the Caucasus. This convergence zone moves off to the northeast as the precipitation moves into the region. A potential vorticity center moves into Turkey from the northwest, strengthening as it does so. It then moves off toward the Caucasus crossing the southeast Black Sea.

Class 4 is dominated by southerly fluxes of water vapor. The MSL pressure has a primary low center northwest of the top of the Persian Gulf with a secondary center in the Fertile Crescent; an Iranian Plateau high is also present. The 500-hPa geopotential has a deep trough extending across the Black Sea toward the Eastern Mediterranean. Midlevel tropospheric winds bend very sharply around the trough axis and are almost southerly throughout the Fertile Crescent region, while the surface winds are strong and head east-southeastward. The Mediterranean MSL pressure low moves in from the west taking a more central Mediterranean line than that seen in previous classes. It stalls for a time at the Eastern Mediterranean coast while the low pressure extends toward the southeast. This leads to the presence of two low centers as described above. After peak precipitation the low centers split with the southern center moving off to the southeast and the northern one moving eastward through the southern Caspian Sea. The midlevel trough axis, anchored north of the Black Sea, develops with the axis oriented toward the southwest and then swings eastward until it reaches the position seen in Fig. 6. It then begins to weaken and broaden. The 500-hPa winds remain almost southerly throughout the event, while the surface winds rotate from southeasterly to northwesterly over the same period. Thus, while the winds are almost parallel ∼12 h before the peak precipitation, significant vertical shear exists at peak precipitation. A strong potential vorticity center moves in from the west, extending over much of the Mediterranean Sea and Turkey. After the peak precipitation the potential vorticity center moves slowly north-northeastward, weakening as it goes.

The differences between these meteorological fields (MSL, 500-hPa geopotential height, and the potential vorticity) are enough to place a storm into one of these classes, thus providing an indication both of how much precipitation to expect from the storm and the direction that the water vapor flux, which the storm relies on, comes from. Note also the presence of a MSL high over the Iranian plateau in all cases. With caution, since this MSL field is interpolated a considerable distance below ground, the presence of this high pressure center is consistent with the mean situation during the dry season, but not the wet season. That is, only during these short-lived precipitation events is the Iranian plateau MSL high present in the wet season. During the dry season this MSL high is associated with the plateau acting as an elevated heat source that causes widespread descent of lower-tropospheric air over the Euphrates–Tigris Valley (Evans et al. 2004).

Using an RCM also provides detail in the vertical direction, allowing the vertical distribution of water vapor fluxes to be investigated. Figure 8 shows the water vapor flux into the box shown in Fig. 1, from the west and from the south, for each class. Classes 1 and 3 have significant westerly influx that extends across the entire latitude range and deep into the atmosphere (up to 450 hPa). For class 1 there is very little contribution from the south, while in the case of class 3 the southerly influx is widespread with the most intense fluxes above the Euphrates–Tigris Valley and slopes of the Zagros Mountains. Class 2 has a more even contribution from the westerly and southerly fluxes with the fluxes being confined to only part of the domain. Note that an area of westerly outflux exists on the slopes of the Taurus Mountains. Class 4 is the only class dominated by southerly influxes. In this class the westerly influxes cover only part of the domain, while the southerly fluxes are more widespread and extend deeper into the atmosphere. The most intense southerly fluxes are, like class 3, concentrated above the Euphrates–Tigris Valley and slopes of the Zagros Mountains. This spatially confined, yet intense, flux manifests itself as a barrier jet west of the Zagros Mountains. Barrier jets are common features during stable airflows against high mountains in midlatitudes. Examples from the same latitude band (i.e., 35°N) have been reported from the Front Range of the Colorado Rockies (Dunn 1992) and the Sierra Nevada (Parish 1982). The limited spatial scale of this barrier jet makes it particularly difficult to resolve by global climate models, which generally use much larger grid spacing. Compared to class 3, class 4 indicates that some of this southerly influx is spilling over the Zagros Mountains toward the east.

6. Orographic precipitation patterns and downscaling

In this section, we discuss the fine spatial pattern of precipitation in relation to the terrain features. Our motivation is to clarify the actual mountain scales that control the precipitation pattern, scales that cannot be resolved on the MM5 25-km grid. To do this, we use the linear downscaling model (LDM) proposed recently by Smith and Barstad (2004). According to this model, there is a close relationship between the atmospheric water vapor flux passing over complex terrain and the small-scale distribution of precipitation. Thus, the fluxes computed in this paper provide the natural input to the downscaling model. The linear model has recently been tested in Oregon (Smith et al. 2005) and in California, Utah, and the Alps (Barstad and Smith 2005). For the current application, we set the cloud time delays in the model to τc = τf = 2000 s to account for slow vapor deposition and falling snow.

The primary terrain feature in our Fertile Crescent region is a ridge of the northern Zagros chain running from the northwest to the southeast corner (Figs. 9 and 10). This ridge lies along the Iraq–Iran border and generally marks the watershed divide between the Tigris and the Urmia Basins. In discussing the details of the precipitation patterns, we focus on case 1 with west-southwestly flow and case 4 with south-southwesterly flow. In both cases, the MM5 results on its 25-km grid show patterns of precipitation that are aligned with this major ridge (Figs. 9a and 10a).

The results from the linear model are shown in Figs. 9b and 10b, where the 1-km high-resolution precipitation pattern has been greatly smoothed to match the 25-km pattern of MM5. Some similarity can be noted. Both models concentrate the precipitation on the main northwest–southeast ridge with significant drying downstream. Also, both models put precipitation on a second ridge near the top of the domain. The region-averaged precipitation amounts are similar.

The two models also differ significantly. The LDM has wetter wet regions and dryer dry regions. The most obvious difference is along the plateau edge on the southeast part of the domain in Figs. 9a,b and 10a,b. MM5 puts little precipitation there compared to LDM. This difference is because the LDM used a domain-averaged WV flux, while the actual MM5 WV flux in this southeast corner is small.

To examine the finescale pattern of the terrain-induced precipitation, we used the full 1-km resolution of the downscaling model to zoom into a small portion of the domain (Figs. 9c and 10c). The subdomain is centered at 36.7°N, 44.8°E and covers an area of approximately 90 km by 100 km. The city of Erbil in northeastern Iraq is west of the southwest corner of the subdomain, while the northeastern corner just touches Lake Urmia in northwestern Iran. On this scale, the individual mountains making up the main ridge [not seen in panels (a) and (b) of Figs. 9 and 10] are well resolved. Just northeast of the subdomain center stands Iraq’s tallest peak, Haji Ibrahim, at 3607 m. On the MM5 grid this mountain is only 2009 m high, while on the LDM grid it reaches to 3586 m.

According to the LDM results in Figs. 9c and 10c, each mountain in the subdomain has its own precipitation maxima: on or just upstream of the terrain peak. Note that the color bar in these figures is changed to capture the higher precipitation rates seen locally. The LDM zoom also displays a remarkably sharp drying gradient on the lee side of the main ridge.

From this exercise in downscaling, we conclude the following:

  • Matching the vapor flux values between MM5 and LDM gives comparable regional-averaged precipitation. The assumption in LDM that the WV flux is uniform over its domain clearly fails in some regions however.

  • The LDM fields suggest that the dynamics and thermodynamics of orographic precipitation are mostly occurring on scales of 5 to 10 km. The MM5 model does not resolve these scales explicitly; thus it deals with spatially averaged physical quantities. Unfortunately, no observations of precipitation heterogeneity or cloud physics processes exist to verify the existence of this small-scale structure. The 1-km precipitation patterns from the LDM model would be helpful in predicting which subwatersheds will receive the precipitation.

7. Conclusions

The study presented here attempts to quantify the significance of southerly water vapor fluxes on precipitation occurring in the eastern Fertile Crescent region. The possibility of this was suggested by previous modeling work and is confirmed through investigation of the NVAP dataset that combines data from several observational platforms. The water vapor fluxes were investigated at high temporal and spatial resolution by using a Regional Climate Model (MM5–Noah) to downscale the NCEP–NCAR reanalysis. Using the ISODATA clustering algorithm the 200 largest precipitation events occurring during the first 5 years of the 1990s and accounting for ∼65% of the entire precipitation were grouped into classes based on the similarity of their water vapor fluxes.

Focusing only on these 200 events and their total precipitation produces the following results. Southerly fluxes were important in 55.5% of the precipitation events, and these events account for 65% of the precipitation. In fact, southerly fluxes were dominant in 24% of events, but these events produced 43% of the total precipitation. Thus, while the majority of precipitation events occurring in the Fertile Crescent require significant water vapor advected from the west, the largest events are dominated by southerly fluxes. This suggests that changes that may affect these southerly fluxes more than the westerly fluxes (e.g., changes in the Indian monsoon, movement of the head of the Persian Gulf, etc.) may have a relatively strong affect on the total precipitation falling in the Fertile Crescent, even though they affect relatively few precipitation events. In either case, comparison with the linear theory orographic precipitation model confirms that the precipitation is partly the direct effect of forced ascent over terrain, possibly with finescale patterns unresolved in MM5.

Acknowledgments

This study was undertaken with the financial support of NASA (EOS/03-0587-0425 and NNG05GB36G) and NSF (ATM-0112354). We appreciate computer time for model runs provided by National Center for Atmospheric Research (NCAR). The research was motivated and assisted by many fruitful conversations with other members of the South-West Asia project team including Roland Geerken, Frank Hole, Ben Zaitchik, Larry Bonneau, and Eddy De Pauw.

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  • Coe, M. T., and Bonan G. B. , 1997: Feedbacks between climate and surface water in northern Africa during the middle Holocene. J. Geophys. Res., 102 , 1108711101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1993: A nonhydrostatic version of the Penn State/NCAR Mesoscale Model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev., 121 , 14931513.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunn, L. B., 1992: Evidence of ascent in a sloped barrier jet and an associated heavy-snow band. Mon. Wea. Rev., 120 , 914924.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, J. P., Smith R. B. , and Oglesby R. J. , 2004: Middle East climate simulation and dominant precipitation processes. Int. J. Climatol., 24 , 16711694.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, J. P., Oglesby R. J. , and Lapenta W. M. , 2005: Time series analysis of regional climate model performance. J. Geophys. Res., 110 .D04104, doi:10.1029/2004JD005046.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., Dudhia J. , and Stauffer D. R. , 1994: A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, 117 pp.

  • Hong, S. Y., and Pan H. L. , 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124 , 23222339.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., Adler R. F. , Morrissey M. M. , Bolvin D. T. , Curtis S. , Joyce R. , McGavock B. , and Susskind J. , 2001: Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeor., 2 , 3650.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jacquemin, B., and Noilhan J. , 1990: Sensitivity study and validation of a land surface parameterization using the HAPEX-MOBILHY data set. Bound.-Layer Meteor., 52 , 93134.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kutzbach, J., Bonan G. , Foley J. , and Harrison S. P. , 1996: Vegetation and soil feedbacks on the response of the African monsoon to orbital forcing in the early to middle Holocene. Nature, 384 , 623626.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lambeck, K., 1996: Shoreline reconstructions for the Persian Gulf since the last glacial maximum. Earth Planet. Sci. Lett., 142 , 4357.

  • Liu, Z., Otto-Bliesner B. , Kutzbach J. , Li L. , and Shields C. , 2003: Coupled climate simulation of the evolution of global monsoons in the Holocene. J. Climate, 16 , 24722490.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahrt, L., and Ek M. , 1984: Influence of atmospheric stability on potential evaporation. J. Climate Appl. Meteor., 23 , 222234.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahrt, L., and Pan H. L. , 1984: A two-layer model of soil hydrology. Bound.-Layer Meteor., 19 , 120.

  • Mlawer, E. J., Taubman S. J. , Brown P. D. , Iacono M. J. , and Clough S. A. , 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102 , 1666316682.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pan, H. L., and Mahrt L. , 1987: Interaction between soil hydrology and boundary-layer development. Bound.-Layer Meteor., 38 , 185202.

  • Parish, T. R., 1982: Barrier winds along the Sierra Nevada Mountains. J. Appl. Meteor., 21 , 925930.

  • Pournelle, J. R., 2003: Marshland of cities: Deltaic landscapes and the evolution of early Mesopotamian civilization. Ph.D. thesis, University of California, San Diego, 314 pp.

  • Randel, D. L., VonderHaar T. H. , Ringerud M. A. , Stephens G. L. , Greenwald T. J. , and Combs C. L. , 1996: A new global water vapor dataset. Bull. Amer. Meteor. Soc., 77 , 12331246.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reisner, J., Rasmussen R. J. , and Bruintjes R. T. , 1998: Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc., 124 , 10711107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schaake, J. C., Koren V. I. , Duan Q. Y. , Mitchell K. , and Chen F. , 1996: A simple water balance model (SWB) for estimating runoff at different spatial and temporal scales. J. Geophys. Res., 101 , 74617475.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, R. B., and Barstad I. , 2004: A linear theory of orographic precipitation. J. Atmos. Sci., 61 , 13771391.

  • Smith, R. B., Barstad I. , and Bonneau L. , 2005: Orographic precipitation and Oregon’s climate transition. J. Atmos. Sci., 62 , 177191.

  • Vettoretti, G., Peltier W. R. , and McFarlane N. A. , 1998: Simulations of Mid-Holocene climate using an atmospheric general circulation model. J. Climate, 11 , 26072627.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weyhenmeyer, C. E., Burns S. J. , Waber H. N. , Aeschbach-Hertig W. , Kipfer R. , Loosli H. H. , and Matter A. , 2000: Cool glacial temperatures and changes in moisture source recorded in Oman groundwaters. Science, 287 , 842845.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yamanaka, T., Shimada J. , and Miyaoka K. , 2002: Footprint analysis using event-based isotope data for identifying source area of precipitated water. J. Geophys. Res., 107 .4624, doi:10.1029/2001JD001187.

    • Search Google Scholar
    • Export Citation
  • Zaitchik, B. F., Evans J. , and Smith R. B. , 2005: MODIS-derived boundary conditions for a mesoscale climate model: Application to irrigated agriculture in the Euphrates basin. Mon. Wea. Rev., 133 , 17271743.

    • Crossref
    • Search Google Scholar
    • Export Citation

Fig. 1.
Fig. 1.

Topography of regional climate model domain (Fertile Crescent study area is outlined in white).

Citation: Journal of Hydrometeorology 7, 6; 10.1175/JHM550.1

Fig. 2.
Fig. 2.

Monthly precipitation in the Fertile Cresent.

Citation: Journal of Hydrometeorology 7, 6; 10.1175/JHM550.1

Fig. 3.
Fig. 3.

Total column-integrated water vapor for 30 Jan 1992.

Citation: Journal of Hydrometeorology 7, 6; 10.1175/JHM550.1

Fig. 4.
Fig. 4.

Monthly distribution of precipitation events ranked by size.

Citation: Journal of Hydrometeorology 7, 6; 10.1175/JHM550.1

Fig. 5.
Fig. 5.

The four water vapor flux–based precipitation event classes.

Citation: Journal of Hydrometeorology 7, 6; 10.1175/JHM550.1

Fig. 6.
Fig. 6.

Mean sea level pressure in hPa (solid lines) with highs and lows indicated and 500-hPa geopotential height in km (dashed lines) at time of peak precipitation for each class.

Citation: Journal of Hydrometeorology 7, 6; 10.1175/JHM550.1

Fig. 7.
Fig. 7.

The 250-hPa potential vorticity at time of peak precipitation for each class.

Citation: Journal of Hydrometeorology 7, 6; 10.1175/JHM550.1

Fig. 8.
Fig. 8.

Vertical cross sections of the water vapor flux crossing the west and south sides of the Fertile Crescent box, 3 h before the precipitation peak (topography is outlined and shown in white).

Citation: Journal of Hydrometeorology 7, 6; 10.1175/JHM550.1

Fig. 9.
Fig. 9.

Precipitation over area of interest for class 1 produced by (a) MM5 and (b) LDM averaged to MM5 grid. (c) The LDM results in full resolution for a 90 km by 110 km subdomain. For all parts, the contours lines represent 500-m elevation contours and vectors show the moisture-weighted wind field.

Citation: Journal of Hydrometeorology 7, 6; 10.1175/JHM550.1

Fig. 10.
Fig. 10.

As in Fig. 9 but for class 4.

Citation: Journal of Hydrometeorology 7, 6; 10.1175/JHM550.1

Table 1.

Number of events (and % of total), total precipitation, and mean uncertainty U for each class.

Table 1.
Save
  • Aqrawi, A. A. M., 2001: Stratigraphic signatures of climatic change during the Holocene evolution of the Tigris–Euphrates Delta, lower Mesopotamia. Global Planet. Change, 28 , 267283.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ball, G. H., and Hall D. J. , 1967: A clustering technique for summarizing multivariate data. Behav. Sci., 12 , 153165.

  • Barstad, I., and Smith R. B. , 2005: Evaluation of an orographic precipitation model. J. Hydrometeor., 6 , 8599.

  • Braconnot, P., Joussaume S. , de Noblet N. , and Ramstein G. , 2000: Mid-Holocene and Last Glacial Maximum African monsoon changes as simulated within the Paleoclimate Modelling Intercomparison Project. Global Planet. Change, 26 , 5166.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and Mitchell K. , 1999: Using GEWEX/ISLSCP forcing data to simulate global soil moisture fields and hydrological cycle for 1987–1988. J. Meteor. Soc. Japan, 77 , 167182.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, F., and Dudhia J. , 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part II: Preliminary model validation. Mon. Wea. Rev., 129 , 587604.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Coe, M. T., and Bonan G. B. , 1997: Feedbacks between climate and surface water in northern Africa during the middle Holocene. J. Geophys. Res., 102 , 1108711101.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1993: A nonhydrostatic version of the Penn State/NCAR Mesoscale Model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev., 121 , 14931513.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dunn, L. B., 1992: Evidence of ascent in a sloped barrier jet and an associated heavy-snow band. Mon. Wea. Rev., 120 , 914924.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, J. P., Smith R. B. , and Oglesby R. J. , 2004: Middle East climate simulation and dominant precipitation processes. Int. J. Climatol., 24 , 16711694.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Evans, J. P., Oglesby R. J. , and Lapenta W. M. , 2005: Time series analysis of regional climate model performance. J. Geophys. Res., 110 .D04104, doi:10.1029/2004JD005046.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., Dudhia J. , and Stauffer D. R. , 1994: A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, 117 pp.

  • Hong, S. Y., and Pan H. L. , 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124 , 23222339.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., Adler R. F. , Morrissey M. M. , Bolvin D. T. , Curtis S. , Joyce R. , McGavock B. , and Susskind J. , 2001: Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeor., 2 , 3650.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jacquemin, B., and Noilhan J. , 1990: Sensitivity study and validation of a land surface parameterization using the HAPEX-MOBILHY data set. Bound.-Layer Meteor., 52 , 93134.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kutzbach, J., Bonan G. , Foley J. , and Harrison S. P. , 1996: Vegetation and soil feedbacks on the response of the African monsoon to orbital forcing in the early to middle Holocene. Nature, 384 , 623626.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lambeck, K., 1996: Shoreline reconstructions for the Persian Gulf since the last glacial maximum. Earth Planet. Sci. Lett., 142 , 4357.

  • Liu, Z., Otto-Bliesner B. , Kutzbach J. , Li L. , and Shields C. , 2003: Coupled climate simulation of the evolution of global monsoons in the Holocene. J. Climate, 16 , 24722490.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahrt, L., and Ek M. , 1984: Influence of atmospheric stability on potential evaporation. J. Climate Appl. Meteor., 23 , 222234.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mahrt, L., and Pan H. L. , 1984: A two-layer model of soil hydrology. Bound.-Layer Meteor., 19 , 120.

  • Mlawer, E. J., Taubman S. J. , Brown P. D. , Iacono M. J. , and Clough S. A. , 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102 , 1666316682.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pan, H. L., and Mahrt L. , 1987: Interaction between soil hydrology and boundary-layer development. Bound.-Layer Meteor., 38 , 185202.

  • Parish, T. R., 1982: Barrier winds along the Sierra Nevada Mountains. J. Appl. Meteor., 21 , 925930.

  • Pournelle, J. R., 2003: Marshland of cities: Deltaic landscapes and the evolution of early Mesopotamian civilization. Ph.D. thesis, University of California, San Diego, 314 pp.

  • Randel, D. L., VonderHaar T. H. , Ringerud M. A. , Stephens G. L. , Greenwald T. J. , and Combs C. L. , 1996: A new global water vapor dataset. Bull. Amer. Meteor. Soc., 77 , 12331246.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reisner, J., Rasmussen R. J. , and Bruintjes R. T. , 1998: Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc., 124 , 10711107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schaake, J. C., Koren V. I. , Duan Q. Y. , Mitchell K. , and Chen F. , 1996: A simple water balance model (SWB) for estimating runoff at different spatial and temporal scales. J. Geophys. Res., 101 , 74617475.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, R. B., and Barstad I. , 2004: A linear theory of orographic precipitation. J. Atmos. Sci., 61 , 13771391.

  • Smith, R. B., Barstad I. , and Bonneau L. , 2005: Orographic precipitation and Oregon’s climate transition. J. Atmos. Sci., 62 , 177191.

  • Vettoretti, G., Peltier W. R. , and McFarlane N. A. , 1998: Simulations of Mid-Holocene climate using an atmospheric general circulation model. J. Climate, 11 , 26072627.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weyhenmeyer, C. E., Burns S. J. , Waber H. N. , Aeschbach-Hertig W. , Kipfer R. , Loosli H. H. , and Matter A. , 2000: Cool glacial temperatures and changes in moisture source recorded in Oman groundwaters. Science, 287 , 842845.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yamanaka, T., Shimada J. , and Miyaoka K. , 2002: Footprint analysis using event-based isotope data for identifying source area of precipitated water. J. Geophys. Res., 107 .4624, doi:10.1029/2001JD001187.

    • Search Google Scholar
    • Export Citation
  • Zaitchik, B. F., Evans J. , and Smith R. B. , 2005: MODIS-derived boundary conditions for a mesoscale climate model: Application to irrigated agriculture in the Euphrates basin. Mon. Wea. Rev., 133 , 17271743.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    Topography of regional climate model domain (Fertile Crescent study area is outlined in white).

  • Fig. 2.

    Monthly precipitation in the Fertile Cresent.

  • Fig. 3.

    Total column-integrated water vapor for 30 Jan 1992.

  • Fig. 4.

    Monthly distribution of precipitation events ranked by size.

  • Fig. 5.

    The four water vapor flux–based precipitation event classes.

  • Fig. 6.

    Mean sea level pressure in hPa (solid lines) with highs and lows indicated and 500-hPa geopotential height in km (dashed lines) at time of peak precipitation for each class.

  • Fig. 7.

    The 250-hPa potential vorticity at time of peak precipitation for each class.

  • Fig. 8.

    Vertical cross sections of the water vapor flux crossing the west and south sides of the Fertile Crescent box, 3 h before the precipitation peak (topography is outlined and shown in white).

  • Fig. 9.

    Precipitation over area of interest for class 1 produced by (a) MM5 and (b) LDM averaged to MM5 grid. (c) The LDM results in full resolution for a 90 km by 110 km subdomain. For all parts, the contours lines represent 500-m elevation contours and vectors show the moisture-weighted wind field.

  • Fig. 10.

    As in Fig. 9 but for class 4.

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