1. Introduction
Precipitation has a direct impact on society. Changes of precipitation on global and regional scales have long been studied and linked to the changing climate (e.g., Allen and Ingram 2002; Dai et al. 1997; Sun et al. 2007; Trenberth et al. 2003). It is important to understand how the global hydrological cycle is changing with increasing atmospheric greenhouse gas loading (Schneider et al. 2010; Stephens and Ellis 2008; Trenberth et al. 2003) and its potential impact on society (Clarke and King 2004; Watkins et al. 2007). Such tasks require a long temporal record of data with global coverage. Satellite-based observations of various components of the global hydrological cycle (e.g., Stephens et al. 2002) and assimilation of observations for dynamically consistent model data with global coverage are thus indispensible (e.g., Bosilovich et al. 2008; Saha et al. 2010).
Remote sensing instruments on board present-day satellite platforms have measured various components of the earth’s hydrological cycle. Retrieval and analysis products are now available for studies of the global hydrological cycle. Among these products are the atmospheric specific humidity (q) measured by the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS; Divakarla et al. 2006; Fetzer et al. 2004, 2006; Susskind et al. 2006) on board the Aqua satellite (launched on 4 May 2002), the high-frequency precipitation (P) syntheses such as the 3B42 algorithm of the Tropical Rainfall Measuring Mission (TRMM; Huffman et al. 2007) and the Global Precipitation Climatology Project (GPCP; Huffman et al. 2009), estimates of surface evaporation fluxes (E) from the Goddard Satellite-based Surface Turbulent Fluxes (GSSTF; Chou et al. 2003; Shie et al. 2009), and the reanalysis q, P, and E products such as those from the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) Modern Era Retrospective-analysis for Research and Applications (MERRA; Bosilovich et al. 2008). No investigation to date has quantified whether these datasets are physically consistent with each other with regard to describing the variability of global hydrological processes.
In this study, we will investigate two datasets of S: 1) the remotely sounded q from AIRS will be used together with the MERRA wind fields to estimate global patterns of S, and 2) the MERRA S directly calculated from the MERRA water vapor budget. This will help determine if their column integrals (Σ) are consistent with the net surface water exchange (P − E) estimated from the TRMM 3B42 or GPCP Ps and GSSTF E as well as from the MERRA output of surface fluxes. Variability of the estimated and reanalysis Σs as well as the observed and reanalysis P − E will be assessed individually by maps of seasonal averages and by wavelet analyses.
2. Data
The S can be estimated from a combination of remotely sounded q and reanalysis winds. To do this, the q in Eq. (1) is obtained from AIRS level 3 (L3) version 5 product at 1° × 1° horizontal resolution (Olsen et al. 2007), and the wind fields are obtained from the MERRA product (version 5.2). Equation (1) is applied to daily data. Since AIRS makes tropical measurements around 0130 and 1330 local time (LT), the daily averaged q within each 1° × 1° grid is actually the average of the two measurements at the two time periods. There are two issues that need to be addressed. The first issue regarding the lack of sampling in cloudy areas hinders the calculation of moisture gradients, so AIRS daily q are averaged onto 10° × 5° longitude–latitude grid boxes that provide acceptable spatial coverage of the q gradients needed in the calculation of S over the globe. The second issue regarding the lack of sampling in cloudy areas is related to cloud-induced sampling biases. Fetzer et al. (2006) showed that the lack of sampling in opaque and precipitating clouds cause the total column water vapor obtained from AIRS retrievals in the tropics to be low by 2%–5% in comparison with the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). These biases can be higher in tropical deep convective regions and the midlatitudes since high filaments of water vapor are disproportionately found in cloudy atmospheric rivers (Ralph et al. 2004) and frontal systems, which are significantly undersampled in AIRS data. Significant biases in the vertical coordinate (Soden and Lanzante, 1996; Tian et al. 2006) can also occur from poor sampling in opaque clouds, even in the tropics, but these effects are not as important for quantifying column-integrated estimates of P − E.
MERRA uses the Goddard Earth Observing System, version 5 (GEOS-5; Rienecker et al. 2008) to assimilate observations, including AIRS radiance data, for the analysis. The 3-hourly instantaneous MERRA winds are averaged daily onto the same 10° × 5° grid boxes as used for AIRS. The MERRA winds are interpolated or averaged from the 42 pressure levels to the 12 AIRS standard pressure levels for q. After these fields are consistently processed on a common grid, the right hand side of Eq. (1) can be calculated on 10° × 5° longitude–latitude grids, where the gradients are computed by finite differencing in spherical coordinates. We chose pt in Eq. (2) to be 200 hPa, the topmost level where AIRS measurements are sensitive to atmospheric water vapor in the tropics (e.g., Fetzer et al. 2008; Gettelman et al. 2004). The contribution to the total water vapor burden from above this altitude is several orders of magnitude smaller and is neglected in this work. Hereafter, the column integral of S in Eq. (2) that uses AIRS q and MERRA winds is referred to as AIRS–MERRA Σ.
While MERRA assimilates AIRS radiance data, it provides fields of q even in cloudy areas. Thus, we also calculate Σ directly from the MERRA’s water vapor budget (i.e., from MERRA’s output of q tendencies from dynamics), referred to as MERRA–MERRA Σ. To compare with the AIRS–MERRA Σ, the MERRA–MERRA Σ is averaged onto 10° × 5° grid boxes from its original resolution of 1.25° × 1.25°.
The P term in Eq. (2) is obtained from the precipitation algorithm 3B-42 (version 6) of TRMM (Huffman et al. 2007) as well as the GPCP 1DD data (version 1.1; Huffman et al. 2001, 2009). The TRMM 3B42 algorithm merges high quality microwave precipitation retrievals from instruments that include the TRMM Combined Instrument (TCI), TRMM Microwave Imager (TMI), Special Sensor Microwave Image (SSM/I), AMSR-E, and Advanced Microwave Sounding Unit-B (AMSU-B) with adjusted precipitation estimates from geostationary observations of infrared brightness temperature (Huffman and Bolvin 2009). The gridded P data are reported at 3-hourly intervals and 0.25° × 0.25° resolution, and extend globally from 50°S to 50°N. The GPCP 1DD data product is a 1° × 1° resolution daily precipitation dataset. It is based on the GPCP version 2.1 satellite-gauge product, which merges global precipitation-gauge analyses with precipitation retrievals from satellites including SSM/I, Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (TOVS), AIRS, and others (Huffman et al. 2009 and references therein). In this study, the P fields are averaged daily on 10° × 5° grid boxes to facilitate comparison to AIRS–MERRA and MERRA–MERRA Σs. The P is also obtained from the MERRA reanalysis and averaged daily on 10° × 5° grid boxes.
The E term in Eq. (2) is obtained from the GSSTF version 2b dataset 2, which are daily latent heat flux estimates based on a bulk flux model with inputs of SSM/I retrievals of 10-m wind speeds, total precipitable water, and bottom-layer (500 m) precipitable water as well as sea surface temperature, 2-m air temperature, and sea level pressure from National Centers for Environmental Prediction/Depart of Energy (NCEP/DOE) reanalysis-2 (Chou et al. 2003; Shie et al. 2009). The daily latent heat flux data are reported at 1° × 1° resolution. We converted the latent heat flux to surface water evaporation flux by dividing the data with the latent heat of evaporation of water. The evaporation fluxes are then averaged on 10° × 5° grid boxes for comparison with the AIRS–MERRA data.
We compare the estimates of Σ from AIRS–MERRA and MERRA–MERRA with three different estimates of P − E: the TRMM 3B42 P minus the GSSTF2b E (referred to as TRMM–GSSTF P − E), the GPCP 1DD P minus the GSSTF2b E (referred to as GPCP–GSSTF P − E), and the MERRA P minus the MERRA E (referred to as the MERRA P − E). In this way, we not only investigate the hydrological consistency between different datasets, but also evaluate the performance of the MERRA P and E with respect to the observationally based estimates.
3. Results
a. Precipitation and evaporation
Climatological averages of the TRMM, GPCP, and MERRA P in 2004–08 are shown in Fig. 1 for boreal winter [December–January–February (DJF) in Figs. 1a–c] and summer [June–July–August (JJA) in Figs. 1d–f]. The geographical distributions of the MERRA P are consistent with those of the TRMM and GPCP P in both seasons. However, the MERRA P is larger over the west Pacific, the Indian, and North American monsoon area.
Geographical distributions of the (a),(d) TRMM 3B42; (b),(e) GPCP; and (c),(f) MERRA precipitation rates (mm day−1). Averaged precipitation rates for (left) DJF of 2004–08 and (right) JJA of 2004–08.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4154.1
Similar to the case of P, the MERRA reproduces geographical distributions of E observed in the GSSTF for both boreal winter and summer (Fig. 2). However, the MERRA has smaller E maxima over the subtropical oceans as well as the storm tracks in both hemispheres.
Geographical distributions of (a),(c) GSSTF2b and (b),(d) MERRA surface evaporation rates (mm day−1). Averages for (left) DJF of 2004–08 and (right) JJA of 2004–08. Missing data in GSSTF2b are indicated as black areas.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4154.1
Seasonal averages of P − E are plotted in Fig. 3 for the TRMM–GSSTF, GPCP–GSSTF, and MERRA. The area with P − E > 0 is where the atmosphere loses water vapor through precipitation. This area includes the intertropical convergence zone (ITCZ), the west Pacific, mid- to high-latitude storm tracks in both hemispheres, and the Indian, North American, and West African monsoon regions. Areas with P − E < 0 are where the atmosphere gains water vapor from the surface and are primarily located over the subtropical oceans. The MERRA P − E patterns are consistent with the observationally based estimates. However, the MERRA has larger P − E over the west Pacific, the Indian, and North American monsoon area, because of the larger P. Since MERRA has smaller E in the subtropical oceans, the MERRA P − E in the subtropical oceans is less negative than those of the TRMM–GSSTF and GPCP–GSSTF. The smaller E in MERRA over the storm tracks in both hemispheres also yields larger P − E compared to TRMM–GSSTF and GPCP–GSSTF over mid- to high latitudes in the winter hemisphere (DJF for the Northern Hemisphere and JJA for the Southern Hemisphere).
Geographical distributions of precipitation minus evaporation (P − E, mm day−1) of (a),(d) TRMM 3B42 P − GSSTF2b E; (b),(e) GPCP P − GSSTF2b E; and (c),(f) MERRA P − E. Averages for (left) DJF of 2004–08 and (right) JJA of 2004–08. Missing data in GSSTF2b E are in black area.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4154.1
b. Apparent water vapor sink
AIRS q is applied to compute the AIRS–MERRA Σ. Fetzer et al. (2006) compared the AIRS precipitable water (column-integrated specific humidity) with AMSR-E’s and found that the biases in AIRS precipitable water depend on the types of cloud present. Compared to the MERRA precipitable water, the AIRS precipitable water has similar geographical distributions in both summer and winter (Fig. 4). The relative difference is about 5%–10%, consistent with the biases reported by Fetzer et al. (2006). The AIRS has less precipitable water over the west Pacific, the Indian, and North American monsoon area, and the Amazon region. This may be caused by the lack of sounding over opaque and precipitating clouds in AIRS.
Geographical distributions of (a),(c) AIRS and (b),(d) MERRA precipitable water vapor (kg m−2). Averages for (left) DJF of 2004–08 and (right) JJA of 2004–08.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4154.1
Geographical distributions of boreal wintertime and summertime averaged Σ (for AIRS–MERRA and MERRA–MERRA) are shown in Fig. 5. The patterns of Σ generally reproduce the seasonal cycle of the global precipitation and evaporation components, including the variations of the location and strength of the ITCZ, the variations of Indian, African, and North American summer monsoons, and the evaporation over the subtropical oceans. The AIRS–MERRA Σ agrees well with the MERRA–MERRA Σ. Detailed discrepancies include the larger Σ in AIRS–MERRA over the southern Amazon of South America during DJF, tropical East Africa, the Southwest, and northern Mexico during the monsoon season in JJA. The MERRA–MERRA Σ is larger than the AIRS–MERRA Σ over the midlatitudes during the respective hemispheric winter seasons. Further tests using MERRA water vapor and winds averaged for different grid box sizes for calculation of advection show that an overestimation of AIRS–MERRA in these regions may be related to the coarse resolution used to compute the water vapor advection. In general, the similarity between the AIRS–MERRA and the MERRA–MERRA Σs suggests that sampling effects caused by the missing data around cloudy areas in the AIRS L3 dataset (Fig. 4) are minimal. However, cloud-induced systematic biases may still exist and cannot be assessed by merely comparing the two datasets. Microwave remote sensing techniques for water vapor in cloudy areas may help reduce such systematic biases and are necessary for more accurately quantifying the atmospheric hydrological cycle.
Geographical distributions of (a),(c) AIRS–MERRA and (b),(d) MERRA–MERRA total water vapor sink (mm day−1). Averages for (left) DJF of 2004–08 and (right) JJA of 2004–08.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4154.1
Zonal averages of AIRS–MERRA and MERRA–MERRA Σs over the oceans are compared to the MERRA, TRMM–GSSTF, and GPCP–GSSTF P − E in Fig. 6. The AIRS–MERRA and MERRA–MERRA Σs are in general agreement with the MERRA P − E. GPCP has higher precipitation rates over the mid- and high latitudes (Figs. 1a–d); therefore, GPCP–GSSTF P − E is larger than TRMM–GSSTF P − E at these latitudes. The AIRS–MERRA and MERRA–MERRA Σs, and the MERRA P − E have reasonable latitudinal variations compared to the TRMM–GSSTF and GPCP–GSSTF (P − E)s. The AIRS–MERRA and MERRA–MERRA Σs, and the MERRA P − E are larger than the observationally based P − E in the tropics during boreal winter, the subtropics in both seasons, and the wintertime hemisphere’s mid- to high latitudes.
Zonal averages over the oceans for AIRS–MERRA (thin solid) and MERRA–MERRA (thin dash) total water vapor sink, MERRA P minus E (thin dash–dot), TRMM 3B42 P minus GSSTF2b E (thick solid), and GPCP P minus GSSTF2b E (thick dash). Data are averaged for (a) DJF and (b) JJA of 2004–08.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4154.1
Geographical distributions of the differences between AIRS–MERRA and MERRA–MERRA Σs as well as the MERRA P − E from the observationally based P − E are shown in Fig. 7 for both boreal winter and summer. Large differences are located over the tropics, subtropics, and the wintertime hemispheres. In the boreal winter, the AIRS–MERRA has larger Σ over the Pacific ITCZ, subtropical Pacific, and Atlantic midlatitude storm track, while the MERRA has larger Σ and P − E over the tropical west Pacific and the high-latitude North Pacific storm track. In the boreal summer, the AIRS–MERRA and MERRA–MERRA Σs, and MERRA P − E all have larger values over the Asian and North American monsoon area, the subtropical east Pacific, and the Southern Hemispheric storm track.
Geographical distributions of differences of (left) AIRS–MERRA, (middle) MERRA–MERRA total water vapor sinks, and (right) the MERRA P − E from the observed P − E. The observed P − E includes TRMM 3B42 P minus GSSTF2b E (the first and third rows) and GPCP P minus GSSTF2b E (the second and fourth rows). The data are averaged for DJF (the first two rows) and JJA (the bottom two rows). Missing data in GSSTF2b are shown in black area.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4154.1
In Table 1, we list the averages of AIRS/MERRA and MERRA–MERRA Σs, MERRA P − E, the TRMM–GSSTF and GPCP–GSSTF P − E for 50°S–50°N over the ocean. The averaged TRMM–GSSTF and GPCP–GSSTF P − E over the ocean are more negative than the Σs and MERRA P − E. This is consistent with the larger evaporation in GSSTF2b over the subtropical ocean compared to the MERRA evaporation (Fig. 2). Also shown in Table 1 are the averages of AIRS–MERRA and MERRA–MERRA Σs, and MERRA P − E over the land and the globe. The averaged MERRA–MERRA Σs are similar to the averaged AIRS–MERRA Σs and have more land–sea contrast than the MERRA P − E. When averaged over 50°S–50°N, AIRS–MERRA and MERRA–MERRA Σs have a value of −0.15 mm day−1, while MERRA – is −0.07 mm day−1. However, when averaged over the whole globe (90°S–90°N), the AIRS–MERRA Σ gives a value of 0.03 mm day−1, the MERRA–MERRA Σ yields 0.12 mm day−1, and the MERRA P − E has a value of 0.17 mm day−1. These numbers are comparable to those obtained by NCEP Climate Forecast System Reanalysis (CFSR), in which the globally averaged P − E is about a few tenths of a millimeter per day (Saha et al. 2010).
Annually averaged total water vapor sinks and P − E (mm day−1) over land and ocean for 50°S–50°N for 2004–08. The rightmost column shows the annually averaged data for the whole globe (90°S–90°N).
c. Closure of the MERRA water vapor budget
Geographical distributions of (a),(e) column-integrated water vapor analysis increment in MERRA; (b),(f) differences of MERRA P from TRMM 3B42 P; (c),(g) differences of MERRA P from GPCP P; and (d),(h) negative differences of MERRA E from GSSTF2b E. Averages for (left) DJF of 2004–08 and (right) JJA of 2004–08. Missing data in GSSTF2b are shown as black areas. Notice that the color scale used for the analysis increment in (a),(e) is about half of those used for the precipitation and evaporation.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4154.1
Since the errors of the TRMM and GPCP precipitations and of the GSSTF2b surface evaporation are unknown, implication of the influence of the calculated ΔP, ΔE, and ΔΣ on 〈dq/dt〉ana becomes less straightforward. Nevertheless, one can still investigate if spatial correlation exists between 〈dq/dt〉ana and the ΔP, ΔE, and ΔΣ calculated using the TRMM, GPCP, and GSSTF2b datasets. In Fig. 8, we plot the MERRA 〈dq/dt〉ana and compare its spatial patterns with ΔP using either TRMM or GPCP, and with −ΔE using GSSTF E. The ΔΣ has been shown in the middle column of Fig. 7. Notice that 〈dq/dt〉ana is the residual of ΔP, ΔE, and ΔΣ, and its scale is about half of the others in Fig. 8.
In the boreal winter, positive MERRA 〈dq/dt〉ana over the tropical west to central Pacific and equatorial West Africa (Fig. 8a) are correlated with the larger model precipitation over these regions, as compared to both TRMM (Fig. 8b) and GPCP P (Fig. 8c). Larger MERRA–MERRA Σ over the equatorial east Pacific compared to Pobs − Eobs (Figs. 7e,f) is associated with the local negative 〈dq/dt〉ana (Fig. 8a).
In the boreal summer, positive 〈dq/dt〉ana over the Asian and North American monsoon regions as well as the tropical west to central Pacific (Fig. 8e) are primarily associated with larger model precipitation over these regions (Figs. 8f,g). Smaller model evaporation is found in the subtropical east Pacific and Atlantic in the Southern Hemisphere (Fig. 8h) compared to observations, and is associated with positive 〈dq/dt〉ana over these regions.
Because of the lack of information on the errors of the observationally based estimates, the correlation between 〈dq/dt〉ana and ΔP or ΔE shown in Fig. 8 may be accidental or may represent actual realization of the model biases. We present the correlation results in this study for the reference of future research.
d. Wavelet spectra of regional hydrological budgets
To further quantify the sources of variability in Σ and the different P − E, we perform wavelet analyses on regionally averaged results. The Σ and P − E fields are averaged over chosen regions (Fig. 9), and the resultant time series are transformed by the Morlet wavelet (Farge 1992). In Fig. 9, regions a–e are along the ITCZ, region f is over the India and Bay of Bengal area (referred to as the India–Bengal area), region g is over tropical South America, and region h is over tropical Africa. The wavelet spectra are shown in Fig. 10. Since surface evaporation data are not available over land, we only show the wavelet spectra of P for TRMM and GPCP over the Indian–Bengal area (Fig. 10f), tropical South America (Fig. 10g), and South Africa (Fig. 10h). We have tested that the wavelet spectra of MERRA P − E over these coastal or continental regions are almost identical to those of MERRA P (precipitation only).
Regions for averaging the total water vapor sinks or P − E for wavelet analyses shown in Fig. 10. The regions are denoted as (a) tropical Indian Ocean (10°S–10°N, 50°–110°E), (b) west Pacific (0°–20°N, 110°–170°E), (c) South Pacific (20°S–0°, 130°E–160°W), (d) east Pacific (0°–15°N, 160°–80°W), (e) tropical Atlantic (0°–10°N, 45°W–0°), (f) the India–Bengal area (10°–20°N, 70°–110°E), (g) tropical South America (10°S–5°N, 80°–45°W), and (h) tropical Africa (10°S–10°N, 10°–40°E). Color contours indicate the annual mean P − E from TRMM 3B42 and MERRA E for 2004–08.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4154.1
Wavelet spectra of the (left to right) AIRS–MERRA, MERRA–MERRA total water vapor sinks, MERRA P − E, TRMM–GSSTF P − E, and GPCP–GSSTF P − E. The unit of the amplitudes is mm day−1. The data are averaged over (a) tropical Indian Ocean, (b) west Pacific, (c) South Pacific, (d) east Pacific, (e) tropical Atlantic, (f) the India–Bengal area, (g) tropical South America, and (h) tropical Africa. The boundary of each region is listed and shown in Fig. 9. The dashed lines indicate the regimes influenced by the edge effect, and the solid lines circle the regime with a 95% confidence level.
Citation: Journal of Climate 24, 24; 10.1175/2011JCLI4154.1
Both AIRS–MERRA and MERRA–MERRA Σs have similar wavelet spectra over all regions. In most regions, the frequency of variations of Σ and P − E mainly falls into three regimes: the annual cycle with a period around 365 days, semiannual variations with periods of 100–200 days, and intraseasonal variations with periods of 30–90 days. Except over tropical Africa, AIRS–MERRA, MERRA–MERRA, and MERRA P − E capture the timing of most of the variations when compared to the TRMM–GSSTF and GPCP–GSSTF P − E. However, the amplitudes of these variations may differ among different datasets, depending on the regions. The AIRS–MERRA, MERRA–MERRA Σs, and the MERRA P − E have weaker amplitudes of intraseasonal variations over the tropical Indian Ocean (Fig. 10a), where the amplitudes are smaller by up to 50% compared to that seen in the TRMM–GSSTF and GPCP–GSSTF P − E. This is consistent with the analysis of water and energy budgets associated with the intraseasonal oscillation of the Indian summer monsoon (Wong et al. 2011).
Over the India–Bengal area (Fig. 10f), the variability in AIRS–MERRA and MERRA–MERRA Σs as well as the MERRA P − E is generally similar to that in the TRMM and GPCP P, although their intraseasonal variations have smaller amplitudes compared to observations. The amplitudes of the annual cycle of the MERRA P − E over this region (as well as over the west Pacific) are larger than any of the others. This discrepancy is also reflected in the larger summertime precipitation rates in the MERRA P − E (Figs. 1f, 3f, 7k,l, and 8f,g) over these two regions.
Over tropical Africa (Fig. 5h), the AIRS–MERRA and MERRA–MERRA Σs have very different spectra from that of the TRMM and GPCP P. The semiannual cycle in the AIRS–MERRA and MERRA–MERRA Σ has weaker variability than the annual cycle. However, the observed P (TRMM and GPCP) have stronger variability in the semiannual cycle than the annual cycle. The MERRA P − E is not consistent with either its own MERRA–MERRA Σ or the TRMM and GPCP P. This may suggest that the moisture modules in the GEOS-5 model may continue to require refinement over tropical Africa and/or the quality of TRMM 3B42 and GPCP precipitation rates over continents may have limitations. Future investigations of precipitation data over land should be carried out (e.g., Dinku et al. 2010, Huffman and Bolvin 2009).
4. Discussion and conclusions
The total water vapor sinks (Σ) in the atmosphere are estimated using water vapor retrievals from the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit (AIRS) with the wind fields from the NASA GSFC Modern Era Retrospective-analysis for Research and Applications (MERRA) reanalysis, as well as from the water vapor budget in the MERRA (AIRS–MERRA Σ and MERRA–MERRA Σ, respectively). Both estimates of atmospheric water vapor sinks are checked for physical consistency with the surface water exchange calculated from the Tropical Rainfall Measuring Mission (TRMM) 3B42 or the Global Precipitation Climatology Project (GPCP) precipitation rate, and the Goddard Satellite-based Surface Turbulent Fluxes (GSSTF2b) surface evaporation rate (TRMM–GSSTF and GPCP–GSSTF P − E, respectively). We also compare the MERRA P − E with the TRMM–GSSTF and GPCP–GSSTF (P − E)s.
The AIRS–MERRA Σ is generally consistent with that from the MERRA water vapor budget, indicating that cloud-induced sampling effects in AIRS retrieved q is minimized when averaging the data with large enough grid boxes (10° × 5° used in this study). However, a systematic retrieval bias within persistently cloudy areas may still be a potential problem in the estimation of Σ. Moreover, averaging q and wind data over large grid boxes before the calculation of finite difference may also generate biases over areas with large variation in topography such as the South American Andes. Future research that focuses on comparison of estimates of water vapor budget using AIRS data with observations of surface water vapor exchange over cloud-cleared area requires collocation of AIRS level-2 product with satellite soundings of cloud and precipitation. Estimations obtained from datasets with higher spatial resolution, accuracy, and a more complete sampling in all cloudy states may lead to further improvements in the global estimates of Σ using remote sensing data.
Compared to the TRMM–GSSTF and GPCP–GSSTF (P − E)s, both AIRS–MERRA and MERRA–MERRA Σs can reproduce distributions and variations of the main components of the atmospheric branch of the global hydrological cycle. These components include net positive water vapor sinks in the intertropical convergence zone (ITCZ), summer monsoons, and midlatitude storm tracks in both hemispheres. However, values of Σ are larger than the observationally based estimates of P − E along the ITCZ, the summer monsoon regions, the subtropical east Pacific in the Southern Hemisphere, and the mid- and high-latitude storm track.
MERRA P − E is in general similar to TRMM–GSSTF and GPCP–GSSTF (P − E)s. However, MERRA has larger P − E over the Asian and North American monsoon area and tropical west Pacific because of the larger MERRA P (compared to TRMM and GPCP), and over the subtropical east Pacific in the Southern Hemisphere and the wintertime storm tracks because of the smaller MERRA E (compared to GSSTF). The discrepancies of MERRA P and E from the observations are correlated with the GEOS-5 analysis water vapor tendency 〈dq/dt〉ana over the Asian and North American monsoon area, tropical west Pacific, and the subtropical east Pacific in the Southern Hemisphere. It is difficult to interpret such correlation as the realization of the model bias, because of the lack of information on the uncertainty of the observationally based estimates of P − E.
The AIRS–MERRA, MERRA–MERRA Σs, and the MERRA P − E have globally averaged values of 0.03, 0.12, and 0.17 mm day−1, respectively. Over the ocean, the TRMM–GSSTF and GPCP–GSSTF averaged (P − E)s are more negative than the averaged AIRS–MERRA, MERRA–MERRA Σs, and the averaged MERRA P − E, because of the larger GSSTF2b surface evaporation over the subtropical ocean.
Wavelet analysis is used to quantify the temporal variability and amplitude of P − E and Σ. The AIRS–MERRA and MERRA–MERRA Σs have reasonable components of temporal variations along the oceanic ITCZ and the Maritime Continent. These components include the annual and semiannual cycles as well as intraseasonal variations. Over the tropical Indian Ocean, the Indian continent, and the Bay of Bengal area, the AIRS–MERRA and MERRA–MERRA Σs have smaller amplitudes of intraseasonal variations, and this is also reflected in the smaller intraseasonal amplitudes of MERRA P − E over these regions. However, over India, the Bay of Bengal, and the tropical west Pacific, the seasonal-cycle amplitudes of MERRA P − E (or P alone) are larger than those of Σ, and TRMM and GPCP P.
Over tropical Africa, both Σs, MERRA P − E (or just P), TRMM and GSSTF P have inconsistent wavelet spectra. This suggests future validation of water vapor and wind profiles, satellite retrieved precipitation rates, and further investigation of the behavior of moisture modules in the assimilation model are necessary over this region.
This work is highly relevant to future Joint Polar Satellite System (JPSS; Lee et al. 2010) Cross-Track Infrared Sounder and Advanced Technology Microwave Sounder (CrIMSS) observations. The “closure” study presented here could be used as a test of the satellite retrieval data with regard to its ability to faithfully represent important climate processes. Furthermore, continuity could be established with AIRS and JPSS to quantify long-term behavior in trends and variability of Σ with climate change.
Correct closure of moisture budgets is essential in climate models and reanalysis products. Therefore, this work is also highly relevant to evaluation of the moisture, wind, precipitation, and evaporation simulations from global climate models as well as climate reanalyses (e.g., Saha et al. 2010) with the satellite-based data products.
Acknowledgments
We thank Michael Bosilovich at NASA Goddard Space Flight Center for the discussion about the MERRA water vapor product. We are grateful to Chung-Lin Shie at NASA Goddard Space Flight Center for the discussion of the usage of GSSTF2b data. We thank NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) for supporting the data, and Goddard Earth Sciences Data and Information Services Center (GES DISC) for distributing the data used in this work. The AIRS, MERRA, TRMM 3B42, and GSSTF2b datasets used in this work can be downloaded from GES DISC. (The GPCP data can be downloaded from the Web site http://precip.gsfc.nasa.gov.) We also thank three anonymous reviewers for comments that improved the manuscript. The research described in this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. This work is supported by the JPL AIRS project, NASA Energy and Water Cycle Study (NEWS), and NASA MEaSUREs.
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