Anthropogenic Impacts on the Water Cycle over Drylands in the Northern Hemisphere

Min Luo aKey Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

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Yuzhi Liu aKey Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China
bCollaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou, China

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Run Luo aKey Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

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Qingzhe Zhu aKey Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

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Jun Guo cJiangsu Climate Center, Jiangsu Meteorological Bureau, Nanjing, China

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Ziyuan Tan aKey Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

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Tianbin Shao aKey Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, China

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Abstract

The effects of anthropogenic factors on the water cycle in drylands of the Northern Hemisphere (NH) are unclear. Here, we used the Community Earth System Model (CESM2.1.0) to quantify the influences of greenhouse gases (GHGs) and anthropogenic aerosols (AAs) on the water cycle and precipitation recycling rate (PRR) over drylands from 1980 to 2014. The corresponding mechanisms are also revealed in this study. The results show that GHGs can intensify the water cycle over drylands by increasing precipitation (P; 0.023 mm day−1) and evapotranspiration (ET; 0.037 mm day−1). Consequently, the negative P − ET (−0.014 mm day−1) is induced because infiltration (I; −0.014 mm day−1) and total water storage (S; −0.011 mm day−1) are decreased, implying a loss of soil water. The PRR is reduced by approximately −0.18% because of the GHG-induced extra water vapor export, which originated from ET. In contrast, AAs can weaken the water cycle over drylands by decreasing P (−0.03 mm day−1) and ET (−0.039 mm day−1). Correspondingly, positive P − ET (0.009 mm day−1) is induced, reflecting an input of soil water. Because of the AA-induced persistent ET from a wetter land and the reduced export water vapor from ET, the PRR increases by approximately 0.15%. Mechanistically, GHGs and AAs can affect the water cycle over drylands by perturbing the descending branches of Hadley circulation in midlatitude regions. Quantifying the climate effects of GHGs and AAs on the regional water cycle improves our understanding of the regional water cycle; the results of this study could also be conducive to the climate predictions for drylands.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yuzhi Liu, liuyzh@lzu.edu.cn

Abstract

The effects of anthropogenic factors on the water cycle in drylands of the Northern Hemisphere (NH) are unclear. Here, we used the Community Earth System Model (CESM2.1.0) to quantify the influences of greenhouse gases (GHGs) and anthropogenic aerosols (AAs) on the water cycle and precipitation recycling rate (PRR) over drylands from 1980 to 2014. The corresponding mechanisms are also revealed in this study. The results show that GHGs can intensify the water cycle over drylands by increasing precipitation (P; 0.023 mm day−1) and evapotranspiration (ET; 0.037 mm day−1). Consequently, the negative P − ET (−0.014 mm day−1) is induced because infiltration (I; −0.014 mm day−1) and total water storage (S; −0.011 mm day−1) are decreased, implying a loss of soil water. The PRR is reduced by approximately −0.18% because of the GHG-induced extra water vapor export, which originated from ET. In contrast, AAs can weaken the water cycle over drylands by decreasing P (−0.03 mm day−1) and ET (−0.039 mm day−1). Correspondingly, positive P − ET (0.009 mm day−1) is induced, reflecting an input of soil water. Because of the AA-induced persistent ET from a wetter land and the reduced export water vapor from ET, the PRR increases by approximately 0.15%. Mechanistically, GHGs and AAs can affect the water cycle over drylands by perturbing the descending branches of Hadley circulation in midlatitude regions. Quantifying the climate effects of GHGs and AAs on the regional water cycle improves our understanding of the regional water cycle; the results of this study could also be conducive to the climate predictions for drylands.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yuzhi Liu, liuyzh@lzu.edu.cn

1. Introduction

Drylands, which occupy approximately 41% of the land surface (White and Nackoney 2003), are defined as regions whose annual potential evapotranspiration (PET) greatly exceeds annual precipitation (P). Generally, the value of P/PET over drylands is less than 0.65 (Hulme 1996; Huang et al. 2017). Drylands are characterized by low fertility soil and water resource deficiencies and face extreme risks of degradation and expansion (Scheffer et al. 2001; Huang et al. 2016; Liu et al. 2018), which could in turn affect the water cycle. Recently, with the enhancement of global warming and human activities, the water cycle over drylands has varied significantly (Zhou et al. 2016; Wang and Qin 2017). However, the changes in water cycle processes over drylands, which are induced by different factors, have not been adequately quantified. Therefore, revealing the attribution of water cycle change over drylands is an urgent issue.

The water cycle is a continuous circulation of water through the ocean, atmosphere (mainly water vapor), land surface, and subsurface. Processes involved in the water cycle are divided into precipitation, evapotranspiration (ET), runoff, and percolation (Trenberth et al. 2011; Yin and Roderick 2020). In recent decades, the mean surface air temperature over drylands has been experiencing an increasing trend (Li et al. 2013; Ji et al. 2014; Luo et al. 2020b, 2021). Global warming will likely lead to more surface precipitation, evapotranspiration, and runoff but less soil water storage (Greve et al. 2018; IPCC 2021). However, there is an observed decrease in precipitation over tropical drylands and northern China due to the changes in atmospheric circulation that are simulated by models (Liu and Allan 2013; Liu et al. 2020b). Generally, the precipitation in midlatitude drylands experienced increasing trends of approximately 1.1 mm decade−1 (Becker et al. 2013). Surface evapotranspiration is dominated by soil water, surface solar radiation, wind speed, temperature, and humidity (Zhang et al. 2019). The mean land evapotranspiration has experienced a significant positive trend of 23 mm decade−1 from 2003 to 2019 (Pascolini-Campbell et al. 2021). Furthermore, the increase in the ET is negatively correlated with the storage in soil water and groundwater (IPCC 2014). The water storage over the drylands of central Asia decreased at a rate of 47.4 mm decade−1 based on satellite datasets and model simulations (Hu et al. 2021). The lowering of groundwater levels in drylands is an important cause of desertification (Sepaskhah et al. 2003; Liu et al. 2018). Under global warming, the components of the water cycle over drylands experiences different changes that combine the effects of GHGs and AAs. It is important to systematically quantify the individual effects of GHGs and AAs on the water cycle over drylands.

The relative contributions of evapotranspiration and water vapor transport to precipitation are called the precipitation recycling rate (PRR), which is an important index that quantifies the strength of land–atmosphere interactions (Brubaker et al. 1993; Dirmeyer and Brubaker 2007; Gao et al. 2020). With the global warming, the PRR is predicted to increase because of increases in precipitation and evaporation (Hua et al. 2016; Gao et al. 2020; Luo et al. 2021). However, weaker water vapor transport over drylands can induce a decrease in PRR (Li et al. 2018; Li and Wang 2020). The changes in PRR depend on the relative contributions of evaporation and external water vapor transport to precipitation. Currently, there is no clear relationship between PRR and precipitation amount (Gao et al. 2020), especially for drylands. Thus, it is important to study their correlations and to quantify the respective contributions of GHGs and AAs to PRR.

The water cycle over drylands is partly constrained by Earth’s energy balance (Allan et al. 2020) and dominated by the changed atmospheric circulation for discrete drylands (Gimeno et al. 2012). Previous studies have shown that scattering and absorptive aerosols can reduce and enhance the water cycle by perturbing the energy balance. The cooling effect of scattering aerosols on the Earth–atmosphere system induces an increase in atmospheric stability and therefore a decrease in precipitation (Ramanathan et al. 2001). Absorptive aerosols can increase precipitation by the hypothesis of an “elevated heat pump” (Lau et al. 2008). An increase in anthropogenic aerosols (AAs) can strongly affect the water cycle over drylands, which depends on a mixture of different aerosol categories. The observed water cycle response over drylands is currently difficult to detect but is expected to emerge and accelerate as aerosol forcing diminishes and warming increases (Allan et al. 2020). Increasing GHG levels lead to global warming, which intensifies the water cycle (Polson et al. 2013; Zhang et al. 2019; Schurer et al. 2020; Ren et al. 2022). The GHG-induced slowdown of overturning circulation is closely related to a weaker Hadley circulation and therefore a decrease in global mean precipitation (Collins et al. 2013; Allan et al. 2020). However, the regional precipitation over drylands, which corresponds to the downward branch of the Hadley circulation, may present different characteristics. In addition, the southward movement of the subtropical westerly jet can enhance the water cycle over the drylands of East Asia by inducing an anomalous warm advection and ascending airflow (Abid et al. 2020). Although many studies have shown the uniform enhancement of the water cycle over drylands under the warming, the corresponding mechanisms are poorly understood due to the many competing physical processes. Moreover, the respective contributions of GHGs and AAs to the water cycle over drylands have not been separated. Therefore, the water cycle over drylands should be estimated comprehensively by combining the atmospheric water balance and the land surface water balance.

In this study, two important scientific issues were addressed: How much do GHGs and AAs systematically contribute to the atmospheric water cycle over drylands in the Northern Hemisphere? And how do GHGs and AAs affect the water cycle via thermal and dynamical processes? Descriptions of the model and methods are presented in section 2. The effects of GHGs and AAs on atmospheric (surface) temperature, precipitation, water vapor transport, and the atmospheric water cycle, together with the corresponding dynamical mechanisms, are analyzed in section 3. Conclusions and discussions are given in section 4.

2. Model and methods

a. Study areas

Drylands are defined as areas where the aridity index (AI; the ratio of potential ET to precipitation) is less than 0.65 (Huang et al. 2017). In this study, drylands were divided into four regions, including East Asia (EA; 31°–52°N, 71°–128°E), west and central Asia (WMA; 31°–52°N, 38°–71°E), North Africa (NAF; 10°–34°N, 18°W–38°E) and North America (NA; 20°–50°N, 98°–124°W), enclosed by red, blue, black, and green rectangles, respectively (Fig. 2).

b. Introduction of datasets

To estimate the AI values over drylands, the potential evapotranspiration and accumulated precipitation of the Climatic Research Unit (CRUv4.0) were utilized. The dataset was constructed using monthly observations across the land areas globally. The spatial resolution of the monthly PET and accumulated precipitation was 0.5° × 0.5° (latitude × longitude). Gridded precipitation is one of the six primary variables derived from observations at global weather stations by angular distance weighting interpolation (Harris et al. 2020). PET is a secondary variable that is estimated from the primary variables using a variant of the Penman-Monteith method (Ekström et al. 2007). The method of calculating PET is shown in Eq. (1). The deviations of CRU occur mainly in the regions with sparser observational data (Harris et al. 2014). A previous study suggested that the results of intercomparisons between CRU, the University of Delaware’s dataset (UDEL), and JRA-55 (Kobayashi et al. 2015) have small inhomogeneities (Harris et al. 2020). The correlation between the global precipitation of CRU and the Global Precipitation Climatology Center (GPCC) (Becker et al. 2013) is approximately 0.87, which is significant at the 95% confidence level (Harris et al. 2020). The reanalysis of CRU has been used to describe global drylands (Liu et al. 2020a; Luo et al. 2021). The PET derived from the CRU data is estimated by the Penman–Monteith equation:
PET=0.408Δ(RnG)+γ900T+273.16U2(eaed)Δ+γ(1+0.34U2),
where Rn (MJ m−2 day−1) denotes net radiation at the crop surface; eaed (kPa) denotes the water vapor pressure deficit for measurement at 2 m; G (MJ m−2 day−1) denotes the soil heat flux, here assumed to be 0; T refers to the mean 2-m temperature (°C); U2 refers to the mean 2-m wind speed (m s−1); Δ denotes the slope of the water vapor pressure curve (kPa °C−1); and γ denotes the psychrometric constant (kPa °C−1). The parameter 900 denotes the coefficient for the reference crop (KJ kg K day−1) (Allen et al. 1994). The parameter of 0.34 denotes the wind coefficient for the reference crop (m s−1) (Allen et al. 1994).

Default emissions of anthropogenic aerosols and well-mixed GHGs from 1750 to 2015 were used in the sensitivity experiments. Emissions of anthropogenic aerosols from industrial, domestic and agricultural activities are derived from the Community Emission Data System (CEDS) (Hoesly et al. 2018). The coupled model includes the surface anthropogenic emissions of black carbon (BC), primary organic matter (POM), the precursor of sulfate (SO2 and SO4), and secondary organic aerosol gases (SOAG). These anthropogenic emissions, which are closely related to local economic and social development, have been widely used in recent studies and CMIP6 sensitivity experiments (Collins et al. 2017; Chen et al. 2018; Luo et al. 2020a). The lower boundary conditions of GHGs (e.g., CO2, N2O, CH4, CFC-11, and CFC-12) are based on snow cover data, ice core data, and archived air data (Meinshausen et al. 2017). These data provide accurate representations of GHGs and help explain climate change. AAs and GHGs are mainly emitted at the surface, but some sulfate emissions and GHGs are introduced vertically, originating from aircraft, in a relatively smaller amount than surface emissions. The spatial resolution of monthly emissions is approximately 0.9° × 1.25° (latitude × longitude). There are three strong sources of AA distributed in East Asia, South Asia, and Europe. Because of the different energy structures and degrees of development in different regions, their principal emissions are also different. Most emissions of BC and POM are distributed in East Asia and South Asia (Collins et al. 2017; Hoesly et al. 2018), while most of the sulfate emissions are concentrated in Europe (Hoesly et al. 2018).

c. Model description and experimental design

In this study, the newer version of the Community Earth System Model (CESM2.1.0) was used to study the effects of GHGs and AAs on the water cycle over drylands. The fully coupled model mainly consisted of the Community Atmospheric Model (CAM6.0), Community Land Model (CLM5.0) and a three-dimensional Parallel Ocean Program (POP2.0). For CAM6.0, a physical radiation scheme of the Rapid Radiative Transfer Model for GCMs (RRTMG) was used to explain the atmospheric radiative fluxes and heating rates in the coupled model (Mlawer et al. 1997). The RRTMG scheme divided the solar spectrum into 14 shortwave bands from 0.2 to 12.2 μm. The longwave bands were divided into 16 bands from 3.1 to 1000 μm (Moncet and Clough 1997). For the shortwave radiative flux, this scheme simulated the extinction (absorption and scattering) of H2O, O3, CO2, CH4, and clouds. For the longwave radiative flux, RRTMG only included the absorption and emission of aerosols and GHGs, except for the recent scattering effects (Mlawer et al. 1997). Some extinction parameters (e.g., optical depth, single scattering albedo, and asymmetry exponents) were also specified in the RRTMG. For CAM6.0, an aerosol module (MAM4) was used to describe the lognormal size distribution of aerosols (Liu et al. 2012). In MAM4, AAs were assumed to be internally mixed with the aging process of POM and BC particles (Jones et al. 2012). In addition, direct and indirect effects were considered in the coupled model. The cloud droplet mass concentration and ice crystal number followed the MG parameterization (Morrison and Gettelman 2008). The Cloud Layers Unified by Binormals (CLUBB) scheme is used to explain physical processes of boundary layer turbulence, shallow convection, and cloud macrophysics in CAM6.0 (Guo et al. 2015). The ZM convection scheme (Zhang and McFarlane 1995) was introduced to calculate the tendencies of water vapor, air temperature, and cloud water.

The fully coupled model performs well in simulating the global climate (Luo et al. 2020a). Thus, we utilized it to estimate the anthropogenic effects on the water cycle over drylands. In this study, three sensitivity experiments were conducted. The control experiment (CTLE) fixed the AA emissions and surface GHG volume concentration in 1850. The GHG experiment (GHGE) had the same parameter configuration as the CTLE experiment, except for fixing the lower boundary condition of GHGs in 2015. The AA experiment (AAE) ran with the same configuration as the CTLE experiment, except for fixing the AA emissions in 2015. To obtain a stable field of the atmosphere and ocean, we ran the fully coupled climate model from 1850 to 2014 with default monthly emissions of GHGs and AAs. The modeling results at multidecadal scales were dominated by anthropogenic signals, while those at yearly or shorter time scales were dominated by internal variability (Kramer and Soden 2016). Here, the anthropogenic impacts on the water cycle over drylands were studied from 1980 to 2014. In addition, sensitivity experiments simulated the global climate in a finite-volume dynamical core with a horizontal resolution of 1.875° × 2.5°. The vertical dimension was divided into 32 hybrid levels. We studied the effects of GHGs and AAs on the water cycle over drylands by estimating the results of GHGE–CTLE and AAE–CTLE. The basic configurations and some schemes of the coupled model are listed in Table 1.

Table 1

Some physical and chemical schemes used in CESM simulations.

Table 1

To estimate the effects of GHGs and AAs on PRR over drylands in recent decades, the model derived from Brubaker et al. (1993) was used in this paper. The Brubaker model is strictly based on the water vapor balance, which is applicable to the estimation of PRR at a long time scale (such as monthly to yearly or longer) (Li et al. 2018; Luo et al. 2021; Yang et al. 2022). A detailed introduction of the PRR conceptual model is shown in Fig. 1.

Fig. 1.
Fig. 1.

Conceptual model of the precipitation recycling in a grid box. The W is the amount of water vapor contained in the air as it moves through the controlled volume. The P denotes the net precipitation falling on the land surface. The ET denotes the net evapotranspiration from the land surface. The Pm and Pa are precipitation of local evaporative and advective origin, respectively. The IN and OUT denote the amounts of input and output water vapor transport, respectively [modified from Brubaker et al. (1993)].

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0037.1

The Brubaker method states three important statistical assumptions: 1) The local evapotranspiration mixes well with the advection of water vapor; 2) the precipitation and evapotranspiration have a uniform distribution; and 3) the local evaporated water vapor content of the air increases linearly and the advected moisture content decreases linearly as the air moves across the regions (Brubaker et al. 1993).

The PRR shows the relative contributions of local ET and external water vapor transport to local precipitation. The definition of PRR is given by
PRR=PmPm+Pa,
where Pm and Pa are precipitations contributed by local evaporative and advective origins, respectively, as shown in Fig. 1. When an air parcel enters the grid shown in Fig. 1, the local evapotranspiration increases linearly. However, the advected water vapor amount decreases linearly as the air moves across the grid. Thus, the average horizontal flux of advected water vapor (Qa) and the average horizontal flux of local evaporation (Qm) over the grid are shown as follows:
Qa=wu+(wuAPa)2=wuAPa2,
where w (precipitable precipitation) denotes the water vapor amount of an air parcel. The term u denotes the horizontal velocity of the air parcel. In this model, the u is vertically averaged with the specific humidity weighting. Therefore, the variable wu is equal to the variable input water vapor transport (IN). The term A denotes the area of the individual grid.
Qm=A2(ETPm).
ET denotes evapotranspiration, which is an output variable of the CESM. ET is calculated by the sum of surface total evaporation and canopy transpiration in our study. In addition, since the atmosphere is assumed to be fully mixed in the horizontal direction, the ratio of advected to locally evaporated water falling as precipitation is equal to the ratio of advected to evaporated moisture remaining in the air parcel.
PaPm=QaQm=wuAPa2A2(ETPm).
It is easy to deduce the variable of PRR by synthesizing Eqs. (2) and (5). In addition, replacing the term of wu with the input water vapor flux (IN), the equation of PRR is shown as follows:
PRR=AETAET+2IN.
In fact, the PRR model of Brubaker is strictly based on the water vapor balance theory (Brubaker et al. 1993; Li et al. 2018). The contribution of output water vapor flux (OUT) has been indirectly included in the PRR variable. A portion of water vapor participates in the process of recycling precipitation; the remaining water vapor can be included in the term OUT. The calculation of IN in this study is given by
IN=lgps100qVdp,
where q denotes the specific humidity, V is the horizontal wind vector (u and υ components), and g is the constant of gravity acceleration (9.8 m s−2). The term of l denotes the length of the boundary through which water vapor is transported into an individual grid. The term “ps” denotes the surface pressure. Here, we assume that the atmospheric pressure at the top of the atmosphere is 100 hPa. To estimate the value of IN, the grid crossing of CESM output can be regarded as the center of a virtual grid, which has the horizontal resolutions of coupled model. The values of specific humidity (scalar) and wind components (vector) at the center are equal to the mean value in a virtual grid. The primary input of water vapor over drylands should be distinguished by the climatology from 1980 to 2014 first. Therefore, the westerly and southerly water vapor transport explain the term IN.

d. Water vapor flux divergence

The sum of advection and divergence of water vapor flux is also called the moisture flux divergence (MFD), which is widely used to estimate the source and sink of water vapor transport (Waldstreicher 1989; Malik et al. 2015). The MFD can be calculated by
MFD=1gps100(uqx+υqy)dp+1gps100q(ux+υy)dp,
where the first term on the right of Eq. (8) denotes the water vapor advection (ADV), which implies the dynamical factor. The second term on the right of Eq. (8) denotes the divergence of the water vapor flux (DIV), which is the thermal factor. The negative values of ADV and DIV denote the amounts of input water vapor, while the positive values denote the amounts of exported water vapor.

3. Results

a. Effects of GHGs and AAs on temperature

The GHG- and AA-induced changes in surface temperature (Ts) in the time period from 1980 to 2014 are shown in Fig. 2. Owing to the heating effect of GHGs, Ts increased by more than 2 K in most drylands of the NH, and the changes were significant above the 90% confidence level (Fig. 2a). The increases in Ts were approximately 2.1, 1.9, 1.8, and 1.6 K over EA, WMA, NAF, and NA, respectively. The mean value of increased Ts over these regions was approximately 1.8 K. In contrast, AAs intercepted part of the radiation that reached the land surface, resulting in a cooling effect of −0.8 K. The maximal value of decreased Ts in the drylands was approximately −2 K in the drylands of the NH (Fig. 2b). The decreases in Ts were approximately −1.0, −0.9, −0.8, and −1.3 K in EA, WMA, NAF, and NA, respectively.

Fig. 2.
Fig. 2.

Mean changes in surface temperature (K) caused by (a) GHGs and (b) AAs in the period from 1980 to 2014. The dots represent the changes are significant above the 90% confidence level. The red, blue, black, and green rectangles denote drylands in East Asia (EA), west and central Asia (WMA), North Africa (NAF), and North America (NA), respectively.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0037.1

GHGs present a strong warming effect on the Earth–atmosphere system. GHGs induced a warming effect on the atmosphere at 200 hPa with a maximal value of 2 K over mid- and low-latitude regions. However, they induced a cooling effect with a maximal value of −0.5 K over high-latitude regions (Fig. 3a). Furthermore, due to GHGs, the air temperatures (AT) at 500 and 850 hPa increased significantly in the range of 1–2 K (Figs. 3c,e). The thermal gradient increased at 200 hPa, while it decreased significantly at 850 hPa in response to increased GHGs. In addition, Vallis et al. (2015) previously revealed the different thermal gradients at low altitudes and high altitudes.

Fig. 3.
Fig. 3.

Mean changes in air temperature (K) at (a),(b) 200, (c),(d) 500, and (e),(f) 850 hPa caused by (left) GHGs and (right) AAs in the time period from 1980 to 2014. The dots represent the changes that are significant above the 90% confidence level. The blank areas in Fig. 2e and 2f indicate the default values at 850 hPa due to the massive topography of landscapes such as the Tibetan Plateau, Greenland, and the Rocky Mountains.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0037.1

AAs presented a significant cooling effect. AT decreased significantly around −2 K over low-latitude regions, while it increased around 1 K over high-latitude regions at 200 hPa (Fig. 3b). The air temperature at 850 and 500 hPa experienced a cooling effect with a maximal decrease in the range from −1 to −1.5 K (Figs. 3d,f).

b. Effects of GHGs and AAs on ET and precipitation

Surface ET is dominated by soil water and the PET (Zhang et al. 2019). When water is plentiful, global warming will likely enhance the surface evapotranspiration (Greve et al. 2018; IPCC 2021). The average increase in ET in the time period from 1980 to 2014 over drylands caused by increased GHGs was approximately 0.032 mm day−1, which corresponds to approximately 3% above the mean ET over drylands (Fig. 4a). The maximum GHG-induced increase in ET (more than 0.2 mm day−1 corresponding to 20% above the mean ET) was mainly distributed in NA. Moreover, the increase in ET over EA was in the range of 0.05–0.15 mm day−1 (corresponding to 6%–17% above the mean ET over drylands). Compared with ET, anomalous precipitation exhibited similar features. The mean value of GHG-induced precipitation over drylands was approximately 0.023 mm day−1, corresponding to 2.5% above the mean precipitation over drylands (Fig. 4b). The maximum increase in GHG-induced precipitation (more than 0.2 mm day−1 corresponding to 22% above the mean precipitation) caused by GHGs was mainly distributed in EA and NA.

Fig. 4.
Fig. 4.

Mean changes (mm day−1) in (a),(c) evapotranspiration and (b),(d) precipitation caused by GHGs and AAs in the time period from 1980 to 2014. The dots represent the changes are significant above the 90% confidence level. The rectangles have the same meaning as in Fig. 2.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0037.1

The mean value of the AA-induced decrease in ET in the time period from 1980 to 2014 was approximately −0.037 mm day−1, corresponding to 4% below the mean ET over drylands (Fig. 4c). The AA-induced decrease in ET fell into the range of −0.05 to −0.2 mm day−1, corresponding to 5%–20% below the mean ET over drylands. As a result of recent growth, ET contributes to approximately 70% of the precipitation on land areas (Xiong et al. 2019). Although more ET can contribute to precipitation, it can equally be stated that more precipitation can lead to more evaporation if the ground becomes wetter. The mean AA-induced change in precipitation over drylands was approximately −0.025 mm day−1, corresponding to 3% below the mean precipitation (Fig. 4d). The maximum AA-induced decrease in precipitation (less than −0.2 mm day−1 corresponding to 22% below mean precipitation) caused by AAs was mainly distributed in the EA.

The GHG- and AA-induced anomalous precipitation is closely related to the GHG- and AA-induced anomalous background of water vapor transport. Most drylands are distributed in midlatitude regions, which correspond to the downward branch of the Hadley circulation. Hence, the GHG- and AA-induced changes in Hadley circulation can affect precipitation over drylands further, which will be analyzed in the following sections.

c. Effects of GHGs and AAs on water vapor transport

Water vapor transport is an important part of the water cycle. Because of the lack of rivers and lakes in drylands, water vapor transport, which is closely related to atmospheric circulations (Zhou and Yu 2005), contributes large amounts of water vapor from oceans to drylands (Liu et al. 2018). Figure 5a describes the climatological water vapor transport during the period from 1980 to 2014. The tropical easterlies and midlatitude westerlies indicate a steady pattern throughout the years. Figure 5b presents the GHG-induced anomaly of water vapor transport. These results show that the tropical easterlies and midlatitude westerlies are significantly enhanced due to the effects of GHGs (vectors in Fig. 5b). Thus, more water vapor will be carried to the drylands of the EA and WMA from the Atlantic Ocean and Mediterranean Sea by enhanced midlatitude westerlies. However, AAs can induce a weaker water vapor transport via their cooling effects. Figure 5c shows the AA-induced anomaly of water vapor transport. These results show that there are easterlies in the midlatitudes and westerlies in the tropics. These anomalous circulations, which are due to the effects of AAs, are opposite to the climatological circulations (vectors in Fig. 5a), implying a weaker water vapor transport.

Fig. 5.
Fig. 5.

The distribution of mean atmospheric column water vapor content (shaded; kg m−2) and horizontal water vapor transport from the land surface to 100 hPa [vectors; kg (m s−1)−1] in the period from 1980 to 2014 (a). Changes in atmospheric column water vapor content and water vapor transport induced by (a) GHGs and (b) AAs in the period from 1980 to 2014. Rectangles are as in Fig. 2.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0037.1

To estimate the changes in water vapor content in the atmosphere over drylands, analysis of the MFD is performed. Figure 6 shows the MFD and its component GHG- and AA-induced anomalies in the time period from 1980 to 2014. In the case of more GHGs, the negative advection term (dynamic factor; Fig. 6a) and divergence term (thermal factor; Fig. 6c) predominate the negative value of the MFD (Fig. 6e). The mean GHG-induced MFD anomalies over the EA, WMA, NAF, NA, and all drylands in the NH are approximately −0.015, −0.013, −0.011, −0.012, and −0.013 mm day−1, respectively. This means net input water vapor over drylands. The results in Fig. 5b show that the enhanced midlatitude westerlies can carry more water vapor from oceans to drylands in the NA, WMA, and EA by advection. The term DIV is a thermal factor that is closely related to convective activities. Generally, the value DIV denotes the vertical water vapor transport. The negative DIV favors ascending airflow and vertical water vapor transport from land to the atmosphere.

Fig. 6.
Fig. 6.

Mean changes (mm day−1) in the (a),(b) advection term, (c),(d) divergence term, and (e),(f) water vapor flux divergence caused by (left) GHGs and (right) AAs in the period from 1980 to 2014. The dots represent the changes are significant above the 90% confidence level. Rectangles are as in Fig. 2.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0037.1

In contrast, the AA-induced positive advection (Fig. 6b) and AA-induced divergence (Fig. 6d) anomalies favor the positive MFD (Fig. 6f), implying a net loss of water vapor over drylands. The average AA-induced MFD over EA, WMA, NAF, NA, and all drylands in the NH are approximately 0.009, 0.002, 0.01, −0.004, and 0.009 mm day−1, respectively. The positive MFD is due to the reduced horizontal water vapor transport (Fig. 5c) and reduced evapotranspiration (Fig. 4c). Consequently, due to the cooling effect of AAs, water vapor transport and evapotranspiration experience a decreasing trend, which is contrary to the effect of GHGs.

d. Effects of GHGs and AAs on PRR

Figure 7a shows the spatial distributions of the climatological PRR over drylands in the time period from 1980 to 2014. The mean value of the PRR over drylands is approximately 3%. Figure 7b describes the spatial distributions of GHG-induced PRRs. The results show that the maximal reduction of the PRR occurred in WMA and NA, with a value of approximately −0.3%, corresponding to a decrease of 10% below the mean PRR (3%). The changes in the PRR over EA ranged from −0.2% to 0.1%. The mean decreased value of the PRR (−0.18%) corresponds to the increased value of ET (0.024 mm day−1) and increased value of precipitation (0.02 mm day−1). This indicates that the contributions of external water vapor transport to precipitation are larger than the contributions of ET to local precipitation. Although the increase in ET enhances water vapor from the land surface to the atmosphere by thermal factors, the increased water vapor is not converted into actual precipitation. Because of the GHG-induced enhanced water vapor transport, the extra ET may be transported to remote regions by advection, leading to a decrease in the contributions of ET to precipitation. The GHG-induced decreased PRR means that the precipitation over drylands shows more dependence on external water vapor transport than on local ET.

Fig. 7.
Fig. 7.

(a) The average distribution of average PRR (%) in the period from 1980 to 2014. Also shown are changes in PRR induced by (b) GHGs and (c) AAs in the period from 1980 to 2014. Rectangles are as in Fig. 2. The dots denote the changes are significant above the 90% confidence level.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0037.1

Figure 7c shows the spatial distributions of AA-induced PRRs. The maximum increase in the PRR is approximately 0.3%, which is distributed in the NAF and WMA. The mean value of increased PRRs (0.148%) corresponds to a decrease in ET (−0.03 mm day−1) and a decrease in precipitation (−0.04 mm day−1) over drylands. The decrease in ET is larger than the decrease in P, resulting in a positive P − ET. Based on the theory of surface water balance, the positive P − ET means more water remains in land. It favors wetter land and persistent ET, and therefore increased PRR over drylands. In addition, the AA-induced water vapor transport and ET over drylands both present decreasing trends. A weaker water vapor transport can further reduce the export water vapor originating from the persistent ET. It can increase the contribution of ET to local precipitation. Because the contribution of ET to precipitation is larger than that of external water vapor transport, the value of the PRR tends to increase.

Furthermore, the GHG- and AA-induced PRR presents a significant positive correlation with changes in precipitation (Fig. 8). The correlation coefficients between them are approximately 0.34 and 0.48, which are significant above the 90% confidence level. The GHG-induced positive precipitation and GHG-induced negative PRR means that more external water vapor is converted to precipitation. The AA-induced negative precipitation and AA-induced positive PRR imply that the water vapor from local ET plays a dominant role. In our results, the anomalous PRR over drylands presented a significant positive correlation with the anomalous precipitation caused by anthropogenic emissions.

Fig. 8.
Fig. 8.

Time series of 3-yr moving-average anomalous PRR (%; blue curve) and precipitation (mm day−1; red curve) induced by (a) GHGs and (b) AAs in the period from 1980 to 2014. The dashed lines are calculated by the linear regression. The correlation coefficient between PRR and precipitation are significant above the 90% confidence level.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0037.1

e. Effects of GHGs and AAs on water cycle

Figure 9a describes the GHG-induced changes in the components of the water cycle in the time period from 1980 to 2014. The results show that precipitation, evapotranspiration, and surface runoff over drylands increase by 0.023, 0.037, and 0.0004 mm day−1, respectively, due to the increase in GHGs. Although the surface runoff is closely related to precipitation, the changes in surface runoff present a small value because of the lack of rivers over drylands. The surface runoff is much smaller than other components of water cycle and is typically neglected. In contrast, the total runoff, infiltration, and total soil water storage from 1980 to 2014 decreased by −0.003, −0.014, and −0.011 mm day−1, respectively. The total runoff is the sum of the surface runoff from land, glaciers, wetlands, and lakes combined with subsurface runoff. Therefore, the total runoff is approximately equal to subsurface runoff due to the small value of surface runoff. The total water storage is the sum of unsaturated soil water and groundwater. Based on the conservation of mass, the difference between infiltration (−0.014 mm day−1) and total soil water storage (−0.011 mm day−1) is the subsurface runoff (−0.003 mm day−1). It means that the infiltration is the sum of total soil water storage and subsurface runoff. Generally, the decreases in infiltration, total runoff, and total soil water storage are closely related to the desertification of drylands. Overall, the effects of GHGs can induce an increase in the principal components (precipitation and evapotranspiration) of the atmospheric water cycle, leading to a stronger water cycle over drylands. The GHG-induced negative PE (−0.014 mm day−1) implies that the net water vapor is transport from land to atmosphere, leading to a wetter atmosphere and drier land.

Fig. 9.
Fig. 9.

The mean changes in water cycle components (mm day−1) induced by (a) GHGs and (b) AAs over drylands in the period from 1980 to 2014. The terms P, E, SR, TR, I, and S represent precipitation, evapotranspiration, surface runoff, total runoff, infiltration, and total soil water storage, respectively. The black short lines denote the root-mean-square errors.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0037.1

The results in Fig. 9b suggest that the principal components (precipitation and evapotranspiration) of the water cycle experience decreasing trends, which means a weaker water cycle caused by AAs. Due to the effects of AAs, the precipitation, evapotranspiration, and surface runoff over drylands decreased by −0.03, −0.039, and −0.0008 mm day−1, respectively. However, the total runoff, infiltration, and total soil water storage from 1980 to 2014 increased by 0.002, 0.01, and 0.008 mm day−1, respectively. The positive value of PE (0.009 mm day−1) induced by AAs favors a drier atmosphere and a wetter land.

Furthermore, the changes in the water cycle are closely related to atmospheric circulation. Generally, the disturbance-driven circulation response depends on the changes in the meridional temperature gradient (Chen et al. 2010; Grise and Medeiros 2016). In the GHGE experiment, the increases in air temperature at middle latitudes are larger than those at low latitudes. This pattern indicates a reduced meridional temperature gradient (baroclinicity) and therefore a reduced surface northerly wind, which is a branch of the Hadley circulation. To estimate the intensity of atmospheric baroclinicity, the equation of F = (−∂/∂y)T is used in this study (Zhang et al. 2021). Figure 10 describes the changes in atmospheric baroclinicity at the surface and at 850 hPa. The results show that atmospheric baroclinicity is reduced by GHGs (red curves), further favoring a weakened Hadley circulation. In addition, the atmospheric baroclinicity is strengthened at the surface and at 850 hPa by AAs (blue curves), which is conducive to an intensification of Hadley circulation.

Fig. 10.
Fig. 10.

The mean changes (averaged over 120°W–120°E) in atmospheric baroclinicity (K per meridional grid) caused by GHGs (red curve) and AAs (blue curve) at (a) the land surface and (b) 850 hPa in the period from 1980 to 2014. Dotted lines represent the mean values of atmospheric baroclinicity.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0037.1

In general, most drylands are distributed in the midlatitude region, corresponding to the downward branch of the Hadley circulation, which is closely related to the water cycle over drylands (Huang et al. 2017). To quantify the anomalous Hadley circulation, the GHG- and AA-induced meridional streamfunctions are shown in Fig. 11. The results in Fig. 11a show a reduction in Hadley circulation caused by GHGs and an enhancement in Hadley circulation caused by AAs in the longitude range from 0° to 40°N. Under a warming climate, weaker Hadley circulation occurs both in climate models and observations (DiNezio et al. 2018; Chemke and Polvani 2019). Theoretically, a weaker Hadley circulation can directly reduce descending airflow over midlatitude regions. It favors an increase in precipitation and therefore a stronger water cycle over drylands. However, the effects of AAs on the water cycle over drylands are different from those of GHGs. Figure 11b describes the increase in streamfunction induced by AAs in the longitude range from 0° to 40°N. This leads to a stronger Hadley circulation, which can directly increase the descending airflow over drylands. Therefore, a stronger Hadley circulation is closely related to the reduced precipitation and water cycle over drylands over midlatitude regions.

Fig. 11.
Fig. 11.

Latitude–height cross section of mean changes (averaged over 120°W–120°E) in streamfunction (shaded; 1010 kg s−1) and perturbed meridional circulations (vectors; m s−1; the vertical velocity × 100) induced by (a) GHGs and (b) AAs in the period from 1980 to 2014.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0037.1

Furthermore, the intensity of the Hadley circulation is also closely related to the southerly water vapor transport from low- to midlatitude regions. Southerly water vapor transport and downward branch airflow over midlatitude region are the two important factors by which Hadley circulation affects the precipitation and water cycle over drylands.

Figure 12 shows the GHG- and AA-induced southerly water vapor transport through the southern boundaries of drylands. The results in Fig. 12a show a decrease of −0.13 mm day−1 in the southerly water vapor transport, which is due to the GHG-induced weaker Hadley circulation. However, the simulated or observed precipitation over drylands experienced an increasing trend under the warming. Therefore, the GHG-induced perturbed ascending airflow in midlatitude regions can offset the negative effect of water vapor transport, resulting in an increase in precipitation and the water cycle over drylands.

Fig. 12.
Fig. 12.

Changes in the southerly water vapor transport (mm day−1) induced by (a) GHGs and (b) AAs in the period from 1980 to 2014. The black short lines represent the root-mean-square errors.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-22-0037.1

Figure 12b shows an increase of 0.04 mm day−1 in the southerly water vapor transport, which is due to the stronger Hadley circulation induced by AAs. Although the increased southerly water vapor transport shows a positive contribution to precipitation over drylands, the perturbed descending airflow induced by AAs over drylands can offset the positive effect of water vapor transport, resulting in a decrease in precipitation over drylands.

4. Conclusions and discussion

In this paper, the GHG-induced and AA-induced water cycle changes over drylands are quantified. By perturbing the water vapor transport and atmospheric circulation, GHGs and AAs show different influences on the water cycle over drylands. The related results could help to deepen our understanding of the water cycle over drylands and it can also help to improve the prediction accuracy of future climate over drylands under global warming.

The GHG-induced intensification of the water cycle shows an increase in precipitation (0.023 mm day−1), evapotranspiration (0.037 mm day−1), and surface runoff (0.0004 mm day−1). Based on the surface water balance, the negative P − ET (−0.014 mm day−1) favors a drier land, resulting in desertification and expansion of drylands. The results also show a decrease in the total runoff (−0.003 mm day−1), infiltration (−0.014 mm day−1), and total soil water storage (−0.011 mm day−1) over drylands. Global warming will likely lead to more surface precipitation, evapotranspiration, and runoff but less soil water storage (IPCC 2021). Because of the warming effects of GHGs, the water vapor transport is enhanced over drylands. It is consistent with the results of Zahn and Allan (2013). In addition, the GHG-induced enhanced water vapor transport can carry the extra ET to remote regions, resulting in the decreased contributions of ET to precipitation. The PRR decreases by −0.18%, which corresponds to a decrease of approximately 6% below the mean PRR (3%) over drylands.

The AA-induced weaker water cycle is characterized by a decrease in precipitation (−0.03 mm day−1), evapotranspiration (−0.039 mm day−1), and surface runoff (−0.0008 mm day−1). The positive value of P − ET (0.009 mm day−1) means that net water vapor is transported from the atmosphere to land, resulting in a wetter land. The results show increase in total runoff (0.002 mm day−1), infiltration (0.01 mm day−1), and the total soil water storage (0.008 mm day−1) over drylands. The AA-induced weaker water cycle and water vapor transport were also confirmed because of their scattering and absorption of solar radiation (Ramanathan et al. 2001; Baek and Lora 2021). Because of the larger contributions of ET to precipitation, there is still an AA-induced increase in the PRR. The value of the PRR increases by 0.148%, which corresponds to an increase of approximately 5% above the mean PRR over drylands.

Although the global water cycle is dominated by thermodynamics (Ramanathan et al. 2001; Allan et al. 2020), the regional water cycle is determined by thermodynamics and dynamical processes (Gimeno et al. 2012). Changes of atmospheric circulation in response to radiative forcing and land surface temperature patterns dominate the water cycle over drylands (Allan et al. 2020). Due to the warming effects of GHGs, the horizontal temperature gradient and atmospheric baroclinicity decrease in the low- and midlatitude regions are favorable conditions to form a weaker Hadley circulation. Under a warming climate, the weaker Hadley circulation occurs both in climate models and observations (DiNezio et al. 2018; Chemke and Polvani 2019). The weaker Hadley circulation can reduce the southerly water vapor transport, which contributes to decreased precipitation over drylands. However, the GHG-induced perturbed ascending airflow of a weaker Hadley circulation induced by GHGs contributes to increased precipitation over drylands. The relative contributions of southerly water vapor transport and perturbed airflow to precipitation have not been estimated in this paper. It is certain, however, that the perturbed ascending airflow in the midlatitude region favors a stronger water cycle over drylands. In contrast, the cooling effects of AAs induce an increased temperature gradient and atmospheric baroclinicity. This favors a stronger Hadley circulation. The AA-induced perturbed descending airflow can directly reduce the precipitation and water cycle over drylands. The results of GHG-induced enhancement of the water cycle and AA-induced reduction of the water cycle are consistent with previous studies (Ramanathan et al. 2001; Polson et al. 2013; Zhang et al. 2019).

The corresponding mechanisms of changed water cycle over drylands are complex due to the many competing physical processes (atmospheric circulations, salinity, land–sea contrast, sea–atmosphere interactions, etc.) (Ma et al. 2018; Yu et al. 2020). The atmospheric circulation responses are less certain than thermodynamic drivers at global scale (Allan et al. 2020). The water cycle over drylands remains an important scientific issue. In particular, the direct impacts of human activities on the water cycle over drylands (irrigation, water abstraction, land use, etc.) are expected to be studied further in the context of dryland expansion and climate change. In addition, the influences of GHGs and AAs on the PRR over drylands have been estimated in detail. The Brubaker model used in this paper is suitable for monthly or longer time scales (Brubaker et al. 1993). Therefore, we combined the Brubaker model and fully coupled model of CESM2.0 to estimate the PRR over drylands. Although the Lagrangian tracer method is better than the Brubaker model without any statistical assumption, it has not been coupled into CESM. The Lagrangian tracer method will be used to estimate the PRR further in our next work. Furthermore, the results are dependent on the realism of the coupled model physical processes, including the runoff, groundwater, and total soil water storage, which should be evaluated and verified in future work.

Acknowledgments.

This research was mainly supported by the National Natural Science Foundation of China (41991231) and jointly supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant XDA2006010301) and the Fundamental Research Funds for the Central Universities (lzujbky-2020-kb02). This work was also jointly supported by the Foundation of Key Laboratory for Semi-Arid Climate Change of the Ministry of Education in Lanzhou University. The authors declare no competing interests.

Data availability statement.

The accumulated precipitation and potential evapotranspiration reanalysis used in this study are openly available from the Climatic Research Unit at https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.05/cruts.2103051243.v4.05/. It is widely used at global land domains except Antarctica (Harris et al. 2014, 2020). In addition, the CESM2.1.0 and compsets of BHIST were used to conduct sensitivity simulations. The initial and boundary condition files are openly available at https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/inputdata/. Since the results of sensitivity numerical simulations are too large to archive and to transfer. Instead, we provide all the information of coupled model and setups of sensitivity experiments needed in section 2 to replicate the same simulations.

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