1. Introduction
Observational studies suggest that the total mass of water vapor increases as a response to the increase in temperature (Trenberth et al. 2005; Wentz et al. 2007; Santer et al. 2007). Similar trends are also seen in the total mass of water vapor from models (Bosilovich et al. 2005; Held and Soden 2006). Unlike the simple relationship between water vapor and temperature, the variations of precipitation are more complex (Trenberth and Shea 2005; Adler et al. 2008; Allan and Soden 2008; Liu et al. 2009; Li et al. 2011; Trammell et al. 2016). On a regional scale, it was found that precipitation increases (decreases) in the wet (dry) areas (Chou and Neelin 2004; Neelin et al. 2006; Allan and Soden 2007; Chou et al. 2009; Li et al. 2011; Durack et al. 2012; Polson et al. 2013; Chou et al. 2013; Trammell et al. 2015; Kao et al. 2017). On a global scale, there is a weak positive trend in the precipitation, with large discrepancies among different studies (Allen and Ingram 2002; Adler et al. 2003; Trenberth et al. 2003; Held and Soden 2006; Gu et al. 2007; Stephens and Ellis 2008; Adler et al. 2008; Liepert and Previdi 2009; Trenberth 2011; Zhou et al. 2011). Recent observational studies (Allan et al. 2010; Li et al. 2011) further suggest that the trend of global water vapor is stronger than the trend of global precipitation, which is consistent with results from some theoretical and model studies (Stephens and Ellis 2008; Allen and Ingram 2002; Emori and Brown 2005; Vecchi and Soden 2007; Richter and Xie 2008). Because the atmospheric water vapor increases faster than the precipitation over the global domain, it suggests that water recycles more slowly over the global domain (Li et al. 2011).
The recycling rate of atmospheric moisture R (Chahine et al. 1997; Li et al. 2011) is defined as a ratio between precipitation P and column water vapor W. Because it includes both precipitation and column water vapor, the recycling rate can be used to monitor the variations in the hydrological cycle. The percentage change of the recycling rate (
2. Data and models
To explore the temporal variations of the recycling rate, the latest datasets from the Special Sensor Microwave Imager (SSM/I) (Wentz 1997; Wentz and Spencer 1998; Wentz and Meissner 2007; Hilburn and Wentz 2008) and the Global Precipitation Climatology Project (GPCP) (Huffman et al. 2009, 2012; Adler et al. 2012) are utilized in this paper. The dataset of SSM/I version 6 (V6) has water vapor over the ocean from 1988 to present, with a spatial resolution of 0.25° × 0.25° (latitude by longitude). The latest version of GPCP, version 2.3 (V2.3), has the global precipitation data from 1979 to present, with a spatial resolution of 2.5° × 2.5° (latitude by longitude). Rain gauge, satellite, and sounding data are utilized to produce GPCP monthly precipitation data. GPCP V2.3 precipitation data are provided by the NOAA Office of Oceanic and Atmospheric Research (OAR) and Earth System Research Laboratory (ESRL) Physical Sciences Division (PSD) (available online at https://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html). The SSM/I V6 water vapor data are provided by Remote Sensing Systems (available online at http://www.remss.com/missions/ssmi/). Monthly mean data from SSM/I and GPCP are used in this paper.
Precipitation and column water vapor from phase 5 of the Coupled Model Intercomparison Project (CMIP5) (Taylor et al. 2012) model simulations are used in the paper to explore the simulation of recycling rates from different models. Observed sea surface temperature is used in the AMIP-type CMIP5 model simulations to drive the models (Taylor et al. 2012). There are 13 CMIP5 models: CAM5, CanESM2, CNRM-CM5, CSIRO Mk3.6.0, GFDL CM3, GISS-E2-R, INM-CM4.0, IPSL-CM5A, MIROC5, HadGEM2-ES, MPI-ESM-LR, MRI-CGCM3, and NorESM1-M (see Table 1 for the institutions associated with the model acronyms). The tropical cloud, moisture, and precipitation fields in these models have been validated extensively against observations (e.g., Jiang et al. 2012; Tian et al. 2013; Stanfield et al. 2016). Because most model simulations ended in 2008, we focus on the variations of the recycling rate from both observations and model simulations from January 1988 to December 2008.
The linear trends (% decade−1) and corresponding confidence levels of maritime recycle rate R, maritime precipitation P, and maritime water vapor W (1988–2008; shown in Figs. 1–3). Confidence levels are listed in parentheses. (Acronym expansions are available online at http://www.ametsoc.org/PubsAcronymList.)
3. Results
Because of the lack of long-term continuous data of water vapor over land and the poor data quality in the polar region as a result of limited in situ measurements to validate the satellite data, the observation results will be compared with model simulations over ocean within 60°S–60°N. Since the recycling rate is related to column water vapor and precipitation, we first explore the temporal variations of maritime column water vapor and precipitation within 60°S–60°N. Figure 1 shows the comparison of temporal variations of maritime column water vapor between the observation and model simulations averaged over 60°S–60°N from 1988 to 2008. El Niño–Southern Oscillation (ENSO) signals have been removed from the time series by a multiple regression method based on the Niño-3.4 index (Li et al. 2011). A low-pass filter is applied to all time series to remove the high-frequency signals, and only signals with periods longer than 3 yr are kept (Jiang et al. 2004). There is an anomaly around 1996–98, which is related to the Pacific decadal variability (Gu and Adler 2013). Gu and Adler (2013) suggest that both Pacific decadal variability and global warming can contribute to the long-term trend in water vapor. The maritime SSM/I water vapor has a strong positive trend of 0.90 ± 0.33% decade−1 over 1988–2008. The linear trend b is estimated using the least squares fitting. The standard error of the linear trend SE(b) is calculated by 
(a) Temporal variations of low-pass-filtered column water vapor averaged over ocean between 60°N and 60°S. Red solid line is low-pass-filtered SSM/I column water vapor time series. Color dashed lines are low-pass-filtered column water vapor time series from CMIP5 models. (b) Trends and uncertainties for low-pass-filtered maritime column water vapor time series from SSM/I and CMIP5 models.
Citation: Journal of Climate 31, 6; 10.1175/JCLI-D-17-0421.1
Figure 2 displays the temporal variations of maritime precipitation between the observation and model simulations averaged over 60°S–60°N from 1988 to 2008. The linear trend of GPCP maritime precipitation is 0.31 ± 0.48% decade−1, which is very weak and not statistically significant. The linear trends of model maritime precipitation demonstrate a relatively large range, from −0.40% to 0.37% decade−1. Figure 2 and Table 1 suggest that most models have weak trends in maritime precipitation with large uncertainties, which are qualitatively consistent with the long-term temporal trend from the observation. However, Fig. 2 shows that there are large discrepancies at the relatively short time scales between the observation and the models. Therefore, the temporal variations of precipitation at the relatively short time scales should be explored in the future, when better models are available.
(a) Temporal variations of low-pass-filtered precipitation averaged over ocean between 60°N and 60°S. Blue solid line is low-pass-filtered GPCP precipitation time series. Color dashed lines are low-pass-filtered precipitation time series from CMIP5 models. (b) Trends and uncertainties for low-pass-filtered maritime precipitation time series from GPCP and CMIP5 models.
Citation: Journal of Climate 31, 6; 10.1175/JCLI-D-17-0421.1
The insignificant, weak temporal trends in precipitation (Fig. 2) and the significant positive trends in column water vapor (Fig. 1) suggest that the maritime recycling rate (i.e., ratio of precipitation to water vapor) decreases with time. Figure 3 shows the comparison of temporal variations of the maritime recycling rates between observations and model simulations averaged over 60°S–60°N from 1988 to 2008. The recycling rate is estimated as the ratio of the GPCP precipitation to SSM/I water vapor, which is shown as the blue solid line in Fig. 3a. Maritime mean recycling rate (blue solid line) demonstrates a negative trend of −0.63% ± 0.40% decade−1 over the past two decades. The negative trend in the maritime recycling rate suggests that the maritime precipitation increases more slowly than the maritime column water vapor, which suggests that the hydrological cycle is recycling slower over 60°S–60°N. Temporal variations of CMIP5 maritime recycling rates are also shown in Fig. 3a. Maritime recycling rates from CMIP5 model simulations all suggest negative trends of the maritime recycling rates, with a range between −1.37% and −0.40% decade−1. Of the 13 models, CAM5 has the strongest negative trend in the maritime recycling rate. Although both observations and models suggest negative trends of the maritime recycling rates on a decadal scale, they differ significantly in relatively short-term interannual variations.
(a) Temporal variations of low-pass-filtered recycling rate averaged over ocean between 60°N and 60°S. Blue solid line is low-pass-filtered recycling rate (ratio of GPCP precipitation to SSM/I water vapor). Color dashed lines are low-pass-filtered recycling rate time series from CMIP5 models. (b) Trends and uncertainties for low-pass-filtered maritime recycling rate time series from observations and CMIP5 models.
Citation: Journal of Climate 31, 6; 10.1175/JCLI-D-17-0421.1
Because the long-term column water vapor data are not available over the land, we cannot compare the global total column water and recycling rate between observations and models. Instead, we compare temporal variations of column water vapor, precipitation, and the recycling rate over both land and ocean between 60°S and 60°N from 13 CMIP5 models. Results are shown in Figs. S1–S3 in the supplemental material. Temporal variations, trends, and uncertainties of column water vapor averaged over land and ocean from 13 CMIP5 models are shown in Fig. S1. All models demonstrate increasing trends of column water vapor for the global domain including both land and ocean. We also explore precipitation over both land and ocean from the 13 CMIP5 models in Fig. S2. Global model precipitation demonstrates weaker trends than the global model column water vapor, which is consistent with the results over ocean only. Additionally, as shown in Fig. S2, model simulations show a much better consistency in simulating precipitation trends when data over land are included, implying models have large discrepancies over ocean in simulating precipitation. Figure S3 demonstrates the temporal variations of the recycling rate from 13 CMIP5 models over both land and ocean at 60°S–60°N. The recycling rates of model simulations covering both land and ocean also demonstrate strong negative trends. The models consistently show that the global recycling rate is slowing down.
In addition to the temporal variations, we also explore the spatial patterns of temporal variations of column water vapor, precipitation, and the recycling rate. Figures 4 and 5 display the spatial patterns of temporal variations of column water vapor and precipitation between the observation and model simulations. The corresponding 90% confidence level areas are shown in Figs. S4 and S5 in the supplemental material. As shown in Fig. 4a, there are positive trends of water vapor over the northern Pacific and northern Atlantic Oceans and along the intertropical convergence zone (ITCZ), as seen in the SSM/I data. Most models can simulate positive trends in the column water vapor over the northern Pacific Ocean, northern Atlantic Ocean, and the ITCZ region, although a few models (e.g., INM-CM4.0, IPSL-CM5A, MPI-ESM-LR, and MRI-CGCM3) do not simulate the positive trends well in the ITCZ. As shown in Fig. 5a, there are positive trends of precipitation over the ITCZ and storm-track regions and negative trends of precipitation over the subtropical regions, as seen from the GPCP precipitation. The strong positive trend of precipitation over the equatorial central and eastern Pacific ITCZ, flanked by negative trends of precipitation over the subtropics, may be a manifestation of the narrowing of the ITCZ in response to global warming (Wodzicki and Rapp 2016; Su et al. 2017). It should be cautioned that the underestimation of light rain over the subtropical ocean in the GPCP dataset (Burdanowitz et al. 2015) possibly affects the results of linear trends of precipitation in the subtropical ocean. Most of the models have captured the pattern that shows the positive percentage change of precipitation over the ITCZ and negative percentage change over the subtropical areas. In addition, the observational data are showing a significant increase over the ITCZ, where some models do not have as intense signals as the observations. The CAM5, CSIRO Mk3.6.0, GFDL CM3, and HadGEM2-ES models show similar strong percentage change as the observations, whereas the INM-CM4.0 model is particularly weak. Most of the models underestimate the intensity of negative precipitation trends over the subtropical areas.
Spatial patterns of temporal variation of water vapor (
Citation: Journal of Climate 31, 6; 10.1175/JCLI-D-17-0421.1
Spatial patterns of temporal variation of precipitation (
Citation: Journal of Climate 31, 6; 10.1175/JCLI-D-17-0421.1
Figure 6 shows the spatial patterns of temporal variation of recycling rates over 1988–2008 from observations and models. The corresponding confidence levels of recycling rate trends larger than 90% are shown in Fig. S6 in the supplemental material. As shown in Fig. 6a, the temporal variations of the recycling rate are positive over the ITCZ and storm-track regions, which suggest the recycling rate of atmospheric moisture has intensified over these regions. Over these regions, the percentage change of the precipitation is larger than that of the column water vapor. As a result, the recycling rate is positive over the ITCZ and storm-track regions, which suggests the hydrological cycle is recycling faster over these regions. The recycling rate displays negative temporal variations over the subtropical regions as a result of a stronger negative trend in the precipitation than in the column water vapor, which suggests that the recycling rate of atmospheric moisture has slowed down in these regions. There are positive recycling rates over the ITCZ regions from most models, although a few models (e.g., CNRM-CM5, INM-CM4.0, MPI-ESM-LR, and NorESM1-M) do not simulate the positive recycling rates well in the tropical Pacific regions. Most models can simulate the positive recycling rate over the high latitudes, although they have difficulties in simulating the locations of the storm tracks. CanESM2, MPI-ESM-LR, and MRI-CGCM3 tend to simulate the negative recycling rates better than the other models over the subtropical areas. It should be mentioned that the physics of the local recycling rate is more complicated than that of the global-mean recycling rate. The temporal variations of the regional recycling rate are determined by the temporal variations of regional precipitation and water vapor (Figs. 4 and 5, respectively), which are strongly influenced by the divergence and convergence of water vapor associated with horizontal motions.
Spatial patterns of temporal variation of recycling rate (
Citation: Journal of Climate 31, 6; 10.1175/JCLI-D-17-0421.1
The trend of the global recycling rate is mainly affected by the trend of water vapor, whereas the trend of the local recycling rate is mainly affected by the trend of precipitation. As shown in Fig. 5, precipitation displays strong and significant trends in different regions. However, different regions demonstrate different and even opposite trends, so the global-average trend of precipitation is not strong and significant. On the other hand, the global water vapor demonstrates strong and significant trend; it is mainly affected by the global temperature via the Clausius–Clapeyron relation. Therefore, the trend of the global recycling rate is dominated by the significant trend of global water vapor. However, the linear trends of regional water vapor are weaker than the regional precipitation trends, as shown in Figs. 4 and 5, and that is why the regional recycling rate is mainly affected by precipitation instead of water vapor.
4. Conclusions
Precipitation data from GPCP and column water vapor data from SSM/I are combined with CMIP5 models to explore the recycling rate from 1988 to 2008. Both observations and models suggest a negative trend of maritime mean recycling rate, which is a result of a weaker trend in the maritime mean precipitation than the mean column water vapor. Overall, all the models simulate similar trends to the observations. The models show consistent trends of maritime mean recycling rate with the observations. On a regional scale, the simulated spatial patterns of the recycling rate capture the dominant features in the temporal variations of the recycling rates: positive trend of the recycling rate over the ITCZ and storm tracks and negative trend of the recycling rate over the subtropical dry areas. This suggests that the CMIP5 models approximately capture the regional- and global-scale recycling rate variations.
The comparisons between observations and simulations also reveal a large discrepancy in the interannual variations of the maritime recycling rate. The analyses of simulations of maritime precipitation and maritime column water vapor further suggest that the discrepancy of the maritime recycling rate is mainly from the poor simulations of maritime precipitation, which suggests that the simulations of relatively short-term variations of precipitation need improvement to better capture the recycling rate of atmospheric moisture. The improved simulations of the recycling rate will help us better understand the physics that govern the temporal variation of hydrological cycle.
Acknowledgments
XJ and YLY were supported by NASA Grants NNX13AC04G and NNX13AK34G. HS and JHJ acknowledge the funding support from NASA NEWS project. HS and JHJ conducted the work at the Jet Propulsion Laboratory, under contract with NASA. LL was supported by NASA ROSES NNH15ZDA001N-PDART program. GJZ was supported by National Science Foundation Grant AGS-1549259. GPCP V2.3 data are provided by the center of Earth System Research Laboratory (http://www.esrl.noaa.gov/psd/data/gridded/data.gpcp.html). SSM/I data are provided by the center of Remote Sensing Systems (http://www.SSM/I.com/SSM/I/SSM/I_browse.html). The authors declare no competing financial interest.
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