The Role of Atmospheric Stabilities and Moisture Convergence in the Enhanced Dry Season Precipitation over Land from 1979 to 2021

Chia-Wei Lan aDepartment of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan

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Chao-An Chen bNational Science and Technology Center for Disaster Reduction, New Taipei, Taiwan

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Min-Hui Lo aDepartment of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan

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Abstract

Between 1979 and 2021, global ocean regions experienced a decrease in dry season precipitation, while the trend over land areas varied considerably. Some regions, such as southeastern China, the Maritime Continent, eastern Europe, and eastern North America, showed a slight increasing trend in dry season precipitation. This study analyzes the potential mechanisms behind this trend by using the fifth major global reanalysis produced by ECMWF (ERA5) data. The analysis shows that the weakening of downward atmospheric motions played a critical role in enhancing dry season precipitation over land. An atmospheric moisture budget analysis revealed that larger convergent moisture fluxes lead to an increase in water vapor content below 400 hPa. This, in turn, induced an unstable tendency in the moist static energy profile, leading to a more unstable atmosphere and weakening downward motions, which drove the trend toward increasing dry season precipitation over land. More water vapor in the low troposphere is because of higher moisture convergence and moisture transport from ocean to land regions. In summary, this study demonstrates the intricate elements involved in altering dry season rainfall trends over land, which also emphasizes the importance of comprehending the spatial distribution of the wet-get-wetter and dry-get-drier paradigm.

Significance Statement

This study found that global land precipitation during the dry season slightly increased from 1979 to 2021, while precipitation over oceans declined. Moist static energy analysis showed a trend toward less stability in areas with increased dry season precipitation and the opposite trend in regions with declining precipitation. Water vapor content trends and dynamic components were the primary controlling mechanism for precipitation trends. Furthermore, the hotspots with pronounced increases or decreases in dry season precipitation reflect local circulation changes influenced by anthropogenic or natural factors.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Min-Hui Lo, minhuilo@ntu.edu.tw

Abstract

Between 1979 and 2021, global ocean regions experienced a decrease in dry season precipitation, while the trend over land areas varied considerably. Some regions, such as southeastern China, the Maritime Continent, eastern Europe, and eastern North America, showed a slight increasing trend in dry season precipitation. This study analyzes the potential mechanisms behind this trend by using the fifth major global reanalysis produced by ECMWF (ERA5) data. The analysis shows that the weakening of downward atmospheric motions played a critical role in enhancing dry season precipitation over land. An atmospheric moisture budget analysis revealed that larger convergent moisture fluxes lead to an increase in water vapor content below 400 hPa. This, in turn, induced an unstable tendency in the moist static energy profile, leading to a more unstable atmosphere and weakening downward motions, which drove the trend toward increasing dry season precipitation over land. More water vapor in the low troposphere is because of higher moisture convergence and moisture transport from ocean to land regions. In summary, this study demonstrates the intricate elements involved in altering dry season rainfall trends over land, which also emphasizes the importance of comprehending the spatial distribution of the wet-get-wetter and dry-get-drier paradigm.

Significance Statement

This study found that global land precipitation during the dry season slightly increased from 1979 to 2021, while precipitation over oceans declined. Moist static energy analysis showed a trend toward less stability in areas with increased dry season precipitation and the opposite trend in regions with declining precipitation. Water vapor content trends and dynamic components were the primary controlling mechanism for precipitation trends. Furthermore, the hotspots with pronounced increases or decreases in dry season precipitation reflect local circulation changes influenced by anthropogenic or natural factors.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Min-Hui Lo, minhuilo@ntu.edu.tw

1. Introduction

As Earth’s atmosphere warms, the saturation water vapor pressure also increases with each degree rise in temperature at a rate of approximately 7% according to the Clausius–Clapeyron relationship (Chen et al. 2002; Held and Soden 2006; O’Gorman and Muller 2010; Santer et al. 2007; Wentz and Schabel 2000). This increase in atmospheric water vapor is a major factor for the intensification of precipitation and more frequent extreme rainfall events that have been observed as the climate warms, although the changes in rate are somewhat inconsistent across different observations (Allan and Soden 2007, 2008; Chen et al. 2012; Li et al. 2015; Liu et al. 2009; O’Gorman and Schneider 2009; Pfahl et al. 2017; Shiu et al. 2012; Sun et al. 2007; Trenberth et al. 2003). Furthermore, climate models predict that global warming will lead to a weakening of large-scale atmospheric circulation, as the atmosphere becomes more stable (Held and Soden 2006; Kjellsson 2015; Vecchi and Soden 2007; Zhang and Song 2006). As a result of upper-tropospheric warming, dry static stability in midlatitudes is expected to increase, thereby increasing atmospheric stability (Frierson 2006).

Recent studies have attempted to capture future precipitation changes, particularly over the tropics, by summarizing them as the “rich-get-richer” mechanism, in which wet regions become wetter and dry regions become drier (Chou and Neelin 2004; Chou et al. 2009; Feng and Zhang 2016; Greve et al. 2014; Greve and Seneviratne 2015; Held and Soden 2006; Roderick et al. 2014). However, the actual pattern of observed precipitation changes might not fully align with this framework. The thermodynamic component associated with enhanced water vapor can lead to increased precipitation minus evaporation (PE) in wet regions and reduced PE in dry regions, but the dynamics related to vertical velocity is contrary to the thermodynamic contribution (Seager et al. 2010). The changes in sea surface temperature gradients and shifts in tropical rain belts over oceans might modify the distribution of wet and dry regions (Chadwick et al. 2013; Mamalakis et al. 2021), and the relative humidity gradient contribution over land can also lead to asymmetry in precipitation changes (Byrne and O’Gorman 2013a, 2015; Wills et al. 2016).

Chadwick (2016) and Kent et al. (2015) have noted the uncertainty surrounding the spatial shifts in convection and convergence related to precipitation changes over land and ocean, which is attributed to uncertainties in model projections of land–sea thermal contrast and sea surface temperature (SST) pattern changes. Due to the smaller heat capacity of land, anthropogenic warming causes faster temperature increases over land, leading to decreased relative humidity compared to over the ocean. This in turn induces greater moisture transport from the ocean to the land and different changes in atmospheric lapse rate, due to the limitation in moisture supply over land (Byrne and O’Gorman 2013b, 2016; Dong et al. 2009; Trenberth and Fasullo 2013; Gimeno et al. 2020; Trenberth et al. 2011). The limitation of moisture over land gives rise to a land–sea thermal contrast and varied hydrological cycles (Roderick et al. 2014).

While only a few land areas show a wet-gets-wetter and dry-gets-drier pattern, a larger percentage of oceanic areas do match this pattern, based on hydrological datasets and CMIP5 model outputs (Greve et al. 2014; Greve and Seneviratne 2015). Furthermore, over the past 61 years, semiarid regions have expanded, as reported by Huang et al. (2015). However, it is worth noting that atmospheric aridity may not precisely indicate the future extent of drylands, as indicated by Berg and McColl (2021) who found no significant trend in future projections. Kumar et al. (2015) argued that under global warming, dry land regions do not experience reduced precipitation since precipitation increases more than evaporation. However, when considering seasonal precipitation changes, dry land regions show a decline in PE during the dry season under global warming.

In recent studies of precipitation changes, it has been observed that the wet season is becoming wetter while the dry season is becoming drier on a global scale (Chou et al. 2013; Chou and Lan 2012; Lan et al. 2019). The changes in temporal precipitation asymmetry over the last 40 years are primarily related to dynamic and thermodynamic components, while the thermodynamic component plays a more significant role in future climate projections (Chou and Lan 2012; Lan et al. 2019). Geng et al. (2020) proposed that the seasonality of SST controls that of tropical precipitation change by modulating both wet-get-wetter and warm-get-wetter effects. However, aridity during the dry season in the future might result from longer dry spells, fewer wet days, and higher temperatures (Wainwright et al. 2021). Continental precipitation changes are driven by compounding forcings such as greenhouse gas emissions, anthropogenic aerosols, land-use changes, and natural climate variabilities (Polson et al. 2013; Sun et al. 2012). Moreover, the decreased seasonality in land precipitation could lead to an enhancement in modeled net primary productivity (Murray-Tortarolo et al. 2017). In contrast, Liang et al. (2020) provided evidence that increasing seasonality in the Amazon River basin is enhancing the seasonality of salinity in the Amazon plume region. This change could potentially impact the ocean’s ecological system due to altered freshwater input.

Table 1 in Chou et al. (2013) indicates that global land areas did not become drier in the dry season from 1979 to 2010, based on both observations and state-of-the-art climate model simulations. However, the complex land–atmosphere interactions involved in the mechanism behind temporal precipitation changes over land during the dry season have not been fully understood (Lan et al. 2019). The reasons behind the discrepancy in temporal precipitation changes between land and ocean, which shows an increase over land but a decrease over the ocean, are also unclear due to these complex interactions. Therefore, this study aims to examine changes in dry season terrestrial precipitation and investigate the potential mechanisms underlying them.

2. Data and methodology

a. Observed precipitation and ERA5 reanalysis

In this investigation, we employed five observed precipitation datasets to estimate the precipitation changes over land from 1979 to 2021. The datasets are as follows: Climatic Research Unit (CRU) (Harris et al. 2014), Global Precipitation Climatology Centre (GPCC) (Becker et al. 2013), University of Delaware Air Temperature and Precipitation (UDel) (Willmott and Matsuura 2001), Global Precipitation Climatology Project (GPCP) (Adler et al. 2003), and NOAA Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) (Xie and Arkin 1997). UDel and GPCC data were available from 1979 to 2017 and from 1979 to 2019, respectively. Two datasets, GPCP and CMAP, were used over the ocean. GPCC, CRU, and UDel are based on rain gauge data only, CMAP relies on satellite data only, and GPCP is a combination of both rain gauge and satellite data. This comprehensive approach not only integrates diverse data sources, including rain gauges and satellite observations, but also accounts for variations in retrieval algorithms used to generate precipitation datasets. This strategy serves to alleviate potential disparities in long-term trends that may arise from differences in algorithmic methodologies.

The land mask was defined using the NCAR Command Language’s land–sea mask data file. Furthermore, we used the fifth major global reanalysis produced by ECMWF (ERA5) (Hersbach et al. 2020) to diagnose atmospheric conditions and for the moisture budget analysis. The wet and dry seasons were defined using an ensemble mean of the precipitation observations during 1979–2021, and the dry and wet months were identified as the months with the lowest and highest 3-month running mean values from multimean precipitation observations for each year and each grid point (Chou et al. 2013; Chou and Lan 2012; Lan et al. 2019). This framework of using the varied wet and dry months in each year can enhance the annual range signal compared to using the mean seasonal cycle. The reanalysis dataset and precipitation observations were interpolated to a uniform horizontal resolution of 1° × 1° using bilinear interpolation.

b. Atmospheric moisture budget analysis

We analyzed the vertically integrated moisture budget in the ERA5 reanalysis to investigate the impact of thermodynamics and dynamics on precipitation changes over land during the dry season. Previous studies (Chou et al. 2009; Held and Soden 2006; Lan et al. 2019, 2016; Seager et al. 2010) have shown that in long-term averages, the time derivative of specific humidity can be disregarded. Due to the continuity equation and ω ≈ 0 at the surface and tropopause, the vertically integrated q∇ ⋅ V is neglected (Tan et al. 2008). These assumptions allow us to break down the advection term into horizontal and vertical components and express the moisture budget equation as follows (Chou and Neelin 2004):
P=VhqωPq+E+res.
Here, P denotes the precipitation, E is the evaporation, Vh is the horizontal wind field, ω is the pressure velocity, and res. is a residual term. The ∂P represents pressure deviation and the angle brackets (〈⋅〉) represent a mass integration throughout the troposphere, and the units for all terms in Eq. (1) are watts per square meter (which can be converted to millimeters per day by dividing by 28.24 at 25°C, 2.45 × 106 J kg−1 divided by 86 400 s). The vertical moisture advection (−〈ωPq〉) is the component of the convergence of the moisture flux associated with vertical motions. The term −〈Vh ⋅ ∇q〉 in Eq. (1) is the horizontal moisture advection, which is the component of the convergence of moisture flux associated with horizontal velocity.
We can separate the time-mean and anomaly components of ω and q in the vertical moisture advection term of Eq. (1) by expressing them as ω=ω¯+ω and q=q¯+q, where the overbar denotes the 1979–2021 seasonal mean and the prime indicates the anomaly from the seasonal mean during the wet and dry seasons. Similarly, we can decompose the precipitation, horizontal moisture advection, and evaporation terms into climatological means and anomalies. Using this decomposition, we can rewrite the precipitation changes in Eq. (1) as
P=Vhqω¯PqωPq¯ωPq+E+res.
Furthermore, we can express the mean state as a moisture balance equation, which is
P¯=Vhq¯ω¯Pq¯+E¯+res.
We disregard the nonlinear term −〈ω′∂Pq′〉 in Eq. (2) because changes in the nonlinear terms, including transient and nonlinear parts (Chou and Lan 2012; Chou et al. 2009; Lan et al. 2019, 2016), during 1979–2021 are nearly unchanged in the reanalysis. The term ω¯Pq in Eq. (2) is related to changes in water vapor content and is referred to as the thermodynamic component, and the third term ωPq¯ on the right-hand side of Eq. (2) is associated with changes in vertical velocity and is referred to as the dynamic component; both terms’ contribution to precipitation changes is also associated with the upward or downward motion climatology, following the previous practice (Chou and Lan 2012; Chou et al. 2009; He and Soden 2016; Lan et al. 2019; Seager et al. 2010).

c. Linear regression trend and statistical significant test

In this study, all of the trend coefficients are calculated based on the linear regression trend, which involves fitting a linear model to the data to estimate the trend over time. The least squares (LS)-based simple linear regression is a classical and extensively used method for detecting linear trends in climate time series. In this approach, the observed climate variable and corresponding time sequences are designated as the response variable Y and the independent variable X, respectively. Through the LS method, the intercept and slope coefficients (β0 and β1) of the best-fitting line are computed, aiming to minimize the squared difference between the observed and predicted Y values.

The linear relationship is represented as follows:
Y=β0+β1X.
The trend inferred from linear regression is determined by the slope coefficient, and its LS estimate is given by
β1=(xx¯)×(yy¯)(xx¯)2.
The sign of the slope coefficient indicates the trend direction, i.e., positive (upward) or negative (downward). The significance of the upward or downward trend is typically evaluated by examining the p value associated with the slope coefficient. A commonly chosen threshold for a significant trend is when the p value is <0.05.

3. Results

a. Precipitation trends over land and ocean from 1979 to 2021

Figure 1 depicts a clear decline in average precipitation over the global oceans during the dry season from 1979 to 2021, while there is a slight increase over global land from 60°S to 90°N. Both time series exhibit a small range of one standard deviation, indicating consistency among the five observational datasets. Table 1 shows that CRU, GPCC, and UDel have slightly upward or neutral trends in land precipitation at rates of 0.131*, 0.065, and −0.001 mm day−1 per century, respectively (trends denoted with an asterisk pass the 95% statistical confidence level of the Student’s t test). The spatial distribution of multimean observations reveals decreasing trends in most ocean regions, except for the western tropical Pacific warm pool region, while the majority of global land regions exhibit increasing precipitation trends (Fig. 2). Precipitation trend maps for each observation during the dry season are illustrated in Fig. S1 in the online supplemental material, demonstrating consistency across each observational dataset. Hotspot regions with particularly strong positive trends include southeastern China, the Maritime Continent, eastern Europe, and eastern North America, while the Amazon, Indian Peninsula, western North America, and western Europe show decreasing trends. We delve into a more detailed examination of these specific regions in section 3c, where we explore the changes in circulation patterns and moisture transport. The percentage of areas with positive precipitation trends is almost double over land compared to oceans, with values of 55.5% and 25.0%, respectively. Figures 1 and 2 reveal a significant reduction in precipitation trends over ocean during the dry season. In contrast, the situation over land is more intricate, with observations indicating either neutral changes or slight increasing trends during the dry season. These contrasting trends and spatial distribution patterns between land and ocean regions challenge the oversimplified notion of a “dry-get-drier” tendency as an accurate descriptor of precipitation trends over land.

Fig. 1.
Fig. 1.

Annual dry season precipitation observations over global ocean and land between 60°S and 90°N from 1979 to 2021. The black solid line represents the ensemble mean of two and five observations for global ocean and land, respectively, while the red lines indicate the linear regression trends of the ensemble mean. The thinner black lines correspond to each individual precipitation observation, and the blue shaded region represents the range of one standard deviation among the two and five precipitation observations. The trends that exceed the 95% confidence level as determined by the Student’s t test are denoted by an asterisk (*) in the top-right corner.

Citation: Journal of Climate 37, 9; 10.1175/JCLI-D-23-0287.1

Table 1.

Trend coefficients of individual precipitation observation data in the dry season over global ocean and land between 60°S and 90°N. The trends passing the 95% statistical confidence level of the Student’s t test are denoted with an asterisk (*).

Table 1.
Fig. 2.
Fig. 2.

Ensemble-mean spatial dry season precipitation trend maps over the (a) ocean and (b) land from 1979 to 2021. The number in the top-right corner indicates the percentage of areas with increasing precipitation. The trends that passed the 95% statistical confidence level of the Student’s t test are stippled.

Citation: Journal of Climate 37, 9; 10.1175/JCLI-D-23-0287.1

To further investigate the land–ocean contrasts in precipitation trends during the dry season, we examine the tendencies of vertical motion and temperature separately for land and ocean areas (Fig. 3). Figure 3a shows the changes in vertical motion, with stronger downward motions over the ocean and weakening downward motions over land, and statistically significant changes below 750 hPa over ocean. Note that the climatological vertical motion in the dry season over land and ocean are both downward. Inconsistent trends in vertical motion between land and ocean may be related to the land–ocean thermal contrast due to global warming, as the temperature increase near the surface is almost three times greater over land than that over the ocean. Mid–upper troposphere temperature trends are similar, with no significant difference between land and ocean (Fig. 3b). The greater warming at low-tropospheric levels over land, combined with the lesser warming at midtropospheric levels, results in a relatively more unstable atmosphere condition, weakening downward motions over land during the dry season, and vice versa for the ocean. Therefore, the vertical structure in relation to the land–sea thermal contrast leads to enhanced precipitation over land and reduced precipitation over oceans during the dry season.

Fig. 3.
Fig. 3.

Trends in the profiles of (a) vertical velocity (Pa s−1 century−1) and (b) temperature (K century−1) in the dry season over global land and ocean from 1979 to 2021 for ERA5. The black dots in each profile (solid lines) represent trends that exceed the 95% confidence level, as determined by the Student’s t test. The shaded regions in the figures indicate the 95% confidence interval.

Citation: Journal of Climate 37, 9; 10.1175/JCLI-D-23-0287.1

b. Mechanisms behind the increasing terrestrial precipitation during dry season

While the average global land precipitation shows a slightly positive trend, as illustrated in Fig. 1, it is essential to note the variability in precipitation change directions across different regions, as seen in Fig. 2. Importantly, these areas where land precipitation is increasing act as a buffer against the dry season’s drying.

To gain deeper insights into the mechanisms governing these positive regions, we have categorized land regions globally based on their precipitation trends during the dry season, whether positive or negative. We then conducted a comprehensive examination, utilizing ERA5 reanalysis data, of the climatology (Fig. 4) and trend profiles (Fig. 5) pertaining to vertical motion, temperature, water vapor, relative humidity, and moist static energy. This approach allows us to better understand the factors contributing to the observed positive trends in land precipitation and their implications for mitigating dry season’s drying. Notably, both positive (P′ > 0) and negative (P′ < 0) areas exhibit climatological downward motion during the dry seasons. The vertical velocity profiles reveal weaker downward motion trends in P′ > 0 regions but upward motion trends in the low troposphere over P′ < 0 regions (Fig. 4a). Moreover, the vertical profiles of climatological temperature and specific humidity in the low troposphere over P′ > 0 regions are lower than those over P′ < 0 regions (Figs. 4b,c), resulting in a more tilted profile of moist static energy indicating more stable atmospheric conditions in the P′ > 0 regions compared to P′ < 0 regions (Fig. 4e).

Fig. 4.
Fig. 4.

Climatological profiles of (a) vertical velocity (Pa s−1), (b) temperature (K), (c) specific humidity (kg kg−1), (d) relative humidity (%), and (e) moist static energy (J) during the dry season over land areas with P′ > 0 (blue) and P′ < 0 (red) from 1979 to 2021 for ERA5. The shading indicates one standard deviation of interannual variation.

Citation: Journal of Climate 37, 9; 10.1175/JCLI-D-23-0287.1

Fig. 5.
Fig. 5.

Trends in the profiles of (a) vertical velocity (Pa s−1 century−1), (b) temperature (K century−1), (c) specific humidity (kg kg−1 century−1), (d) relative humidity (% century−1), and (e) moist static energy (J century−1) during the dry season over land areas with P′ > 0 (blue) and P′ < 0 (red) from 1979 to 2021 for ERA5. The black dots in each profile (solid lines) represent trends that exceed the 95% confidence level, as determined by the Student’s t test. The shaded regions in the figures indicate the 95% confidence interval.

Citation: Journal of Climate 37, 9; 10.1175/JCLI-D-23-0287.1

In regions where P′ > 0, the vertical velocity exhibits weaker climatological downward motion above 850 hPa and a negative trend from 950 to 150 hPa. Conversely, in regions where P′ < 0, stronger climatological subsidence is observed above 850 hPa, along with positive trends from 850 to 200 hPa (Figs. 4a and 5a). These differences in vertical motion are statistically significant between 500 and 350 hPa and around 750 hPa. The differences in vertical motion changes are associated with changes in atmospheric stability, which are often a result of variations in temperature, specific humidity, and moist static energy. To investigate the factors accounting for the differences in omega profile over P′ > 0 and P′ < 0 land areas during the dry season, we analyzed the temperature and water vapor trends, as well as the moist static energy trends. The temperature trend profiles over regions with P′ > 0 and P′ < 0 display similar structures with no significant difference between these two areas, showing an increase of about 2.5 K century−1 at the surface and 2.0 K century−1 in the midtroposphere (Fig. 5b). However, there is a strong contrast in the water vapor below 400 hPa, with slight increases for P′ > 0 areas and noticeable declines for P′ < 0 areas (Fig. 5c). Although their 95% confidence intervals do overlap to some extent, the trends still indicate a higher probability of positive and negative trends for P′ > 0 and P′ < 0 areas, respectively.

In Fig. 5, the slight increase in water vapor below 400 hPa over P′ > 0 land areas within 43 years is presumably attributed to the toward neutral or unstable tendency of atmospheric conditions rather than temperature changes. This neutral or unstable atmospheric environment results from the unstable tendency in the moist static energy profiles, which causes the weakening subsidence in the dry season and brings more precipitation. These findings are consistent with previous studies that suggest water vapor vertical profiles above the boundary layer play the primary role in determining deep convective activity and precipitation rates (Holloway and Neelin 2009, 2010). The moist static energy trend profiles are one of the primary factors associated with atmospheric stability, which in turn affects local precipitation changes, particularly for tropics and subtropics (Lan et al. 2019; Lin et al. 2016).

It is important to highlight a key observation from Fig. 5e: while there is an overlap in the 95% confidence intervals for the moist static energy profile trends in these two areas, particularly below 500 hPa, our primary focus was not to delve into significant differences between P′ > 0 and P′ < 0. Instead, our main emphasis was on analyzing and discussing the factors contributing to the increased precipitation over land. This approach stems from the understanding that even when atmospheric stability tends toward a neutral or unstable condition in P′ > 0 regions, it can play a crucial role in preventing certain land areas from experiencing drying, thus counteracting the trend of dryness. This result prompts us to furtherly explore the source of increased water vapor in P′ > 0 land areas.

Our analysis also revealed that the climatological relative humidity is lower in the low troposphere for P′ < 0 areas compared to P′ > 0 areas (Fig. 4d), primarily due to higher temperatures. Additionally, the rate of change of relative humidity is decreasing more in P′ < 0 areas than in P′ > 0 areas (Fig. 5d). This is attributed to a decrease in water vapor content, combined with a similar increasing rate of temperature and higher climatological air temperature over the 43-yr period. As a result, the relative humidity in P′ < 0 areas becomes much smaller than that in P′ > 0 areas, indicating that the critical role Clausius–Clapeyron relationship between these two regions does not remain constant over the 43-yr period. We believe that this distinct contrast in the climatology of relative humidity profiles could potentially explain the differences in precipitation changes. Notably, regions with higher relative humidity are more conducive to condensation processes, which can be influenced easily by various dynamic and thermal mechanisms.

We analyzed the atmospheric moisture budget using ERA5 data in the dry season over the P′ > 0 and P′ < 0 areas. Our results indicate that P′ > 0 areas exhibit upward motion anomalies in midtroposphere vertical motion, while the opposite is observed for P′ < 0 areas (Fig. 6). These results suggest that the distribution of precipitation trends is controlled by the dynamic component linked to changes in vertical motion. An investigation of the trend profiles reveals that the discrepancy in vertical motion trends between P′ > 0 and P′ < 0 areas may be attributed to the differences in moisture content below 400 hPa, which aligns with the changes of 〈q〉 in Fig. 6. The increased water vapor in the mid-to-low troposphere can be linked to either increased air temperatures or moisture convergence. However, we observed that the trend coefficients of surface and vertical temperature are almost identical in both regions (as shown in Figs. 5b and 6). The moisture budget analysis revealed that the trend coefficients of moisture flux divergence exhibit negative trends for P′ > 0 and positive trends for P′ < 0. In other words, the amplified (reduced) water vapor is associated with the robust horizontal moisture flux convergence (divergence). This could be linked to changes in the local circulation pattern changes. Note that due to the complex factors influencing the moisture budget over land, the trend maps of each term in the atmospheric moisture budget analysis demonstrate heterogeneous spatial changes, as illustrated in Fig. S2.

Fig. 6.
Fig. 6.

Trend coefficients for each term in the moisture budget analysis (W m−2 century−1), vertically integrated moisture (kg kg−1 century−1), vertical motion at 500 hPa (1000 Pa s−1 century−1), 2-m temperature (K century−1), and the moisture flux divergence 〈∇ ⋅ Vhq〉 (W m−2 century−1) in the dry season over land areas with P′ > 0 and P′ < 0 from 1979 to 2021. Black squares indicate the trends that exceed the 95% confidence level as determined by the Student’s t test.

Citation: Journal of Climate 37, 9; 10.1175/JCLI-D-23-0287.1

c. Precipitation changes correspond to moisture transport

We have identified that increased precipitation during the dry season in specific land areas is attributed to greater horizontal moisture flux convergence in the mid-to-lower troposphere, which is believed to be associated with local circulation changes. To delve deeper into this phenomenon, our investigation has homed in on five regions—southeastern China, eastern Europe, eastern North America, South Asian monsoon, and western North America—where there are noticeable trends in dry season precipitation.

Utilizing ERA5 reanalysis datasets, we have scrutinized the corresponding local dry season circulation trends and charted their spatial variations, with a focus on the lowest month of the mean seasonal precipitation climatology (a fixed dry season) as opposed to the previously considered variable dry season. The rationale behind this shift lies in our discussion of vector changes, which necessitates a consistent temporal reference across all locations, hence requiring the entire spatial domain to be aligned to the same month.

  1. Southeastern China: The typical dry season in southeastern China falls in November and December, during the winter monsoon season, where northeasterly winds are prevalent. However, Fig. 7a shows that the region’s trend is a weakening winter monsoon circulation associated with anomalous southwesterly flow from the ocean. Such wind anomalies cause more water vapor to be transported from the South China Sea to inland China, resulting in higher moisture convergence and increased precipitation during the dry season.

  2. Eastern Europe: The dry season in eastern Europe occurs between March and April, where the mean state experiences westerly low-level wind flow. Nevertheless, as depicted in Fig. 7b, there is a trend toward stronger northerly anomalies during the dry season, bringing more oceanic moisture from the Arctic.

  3. Eastern North America: The dry season in eastern North America is between September and October when the climatological winds are from the southwest. However, Fig. 7d shows a trend toward anomalous easterly flow from the North Atlantic Ocean, transporting more water vapor inland during the dry season and leading to higher precipitation.

  4. South Asian monsoon region: Precipitation decreases during the dry season (December–February), similar to western North America. This trend contrasts with the previous three regions discussed in this section. This decline is attributed to an enhanced monsoon circulation (Fig. 7d). In summary, these abnormal winds in these specific land areas are accompanied by varying degrees of moisture convergence.

  5. Western North America: According to climatology, the dry season in western North America spans from June to August. In western North America, the northerly circulation changes fail to transport additional water vapor to the inland regions due to the low water vapor content in high-latitude areas compared to tropical oceans, as shown in Fig. 7e. This causes the reduced precipitation in the dry season of western North America.

Fig. 7.
Fig. 7.

The spatial trend maps of the 3-month dry season averages in the moisture flux at 850 hPa (wind barbs; kg kg−1 m s−1 century−1) and vertically integrated moisture flux divergence (shading; W m−2 century−1) of ERA5 for each region (red box) from 1979 to 2021.

Citation: Journal of Climate 37, 9; 10.1175/JCLI-D-23-0287.1

Figure 7 is not intended to depict uniform convergence over entire regions. Instead, they aim to illustrate that while there may be some convergence in specific areas such as North America and eastern Europe, the crucial point shared across these figures is the moisture flux component. In summary, these figures aim to illustrate that moisture convergence is closely tied to changes in local circulation patterns, facilitating the transport of water vapor from the ocean to the land. This process emerges as a primary factor contributing to the increasing water vapor in the low troposphere, subsequently inducing instability of the moist static energy.

4. Discussion

a. The consistency between different reanalysis datasets

We have conducted additional analyses using the MERRA-2 and NCEP R2 reanalysis datasets, as depicted in Figs. S3 and S4. The contrast between land and ocean, as illustrated in Fig. S3, reveals a higher rate of warming below 800 hPa over land compared to the ocean during the dry season, a pattern consistent with the findings from the ERA5 reanalysis.

The trend profiles of vertical motion from these two reanalysis datasets indicate the regions where P′ > 0 exhibit less strengthening of downward motion compared to regions where P′ < 0. This characteristic diverges from the ERA5 results, which show a weakening of downward motion in P′ > 0 regions. However, temperature changes show similarity in both types of regions. Moreover, P′ > 0 regions exhibit more water vapor in the low troposphere compared to P′ < 0 areas in both reanalysis datasets (Fig. S4). Consequently, in the MERRA-2 and NCEP R2 reanalysis datasets, the profile trends in P′ > 0 regions tend to create unstable conditions and enhance precipitation during the dry season over land. Therefore, across all three reanalysis datasets, there is a consistent observation that the dynamic component associated with moist static energy trend profiles plays a pivotal role in intensifying dry season precipitation over land, rather than the surface latent heat flux. Additionally, it is worth noting that surface latent heat flux is not consistently aligned among these three reanalysis datasets.

Despite potential disparities in reanalysis precipitation, as shown in Fig. S5, particularly with some biases in tropical regions, the atmospheric variables exhibit a higher degree of consistency. This underscores why we rely on precipitation observations rather than reanalysis precipitation and utilize atmospheric variables from the reanalysis datasets.

b. Regional contrast in North America

While previous analyses focused on a global scale, a closer examination at the regional level reveals significant trends. Climatological wet regions, such as the eastern United States, are experiencing notable increases, while climatological dry regions, like the western United States, are witnessing significant decreases in precipitation. To gain deeper insights into these trends, we conducted a comprehensive investigation of dry season tendencies in both regions of North America.

In the eastern part of North America, the water vapor profile tendencies remain close to neutral at all levels, and the moist static energy profiles exhibit minimal changes (Fig. S6). In contrast, western North America displays a distinct reduction in water vapor below 700 hPa, contributing to a more stable moist static energy profile (Fig. S7). While vertical motion alone may not fully explain the precipitation disparities between these two regions, the atmospheric conditions tend to favor more convection in eastern North America compared to western North America.

Regarding changes in circulation patterns and moisture flux, alterations in circulation patterns in eastern North America facilitate the transport of more moisture from the ocean, thereby maintaining a certain level of water vapor during the dry season (Fig. 7c). Conversely, in western North America, changes in northerly circulation fail to transport additional water vapor inland, primarily due to the lower water vapor content in high-latitude areas relative to tropical oceans, as evident in Fig. 7e. Consequently, this lack of moisture in the low troposphere does not create an unstable atmospheric environment conducive to increased precipitation during the dry season over the western United States.

c. The consistency between observational precipitation datasets

In response to concerns about the reliability of precipitation data, we have addressed this issue by incorporating Figs. S8 and S9 into our analysis. These figures depict the variations in station counts per grid cell for the GPCC and CRU datasets over two distinct 10-yr periods—the initial and the most recent. Notably, a multitude of terrestrial areas, with North America being particularly prominent, display a reduction in station coverage during these periods. Maintaining consistent station counts is of paramount importance in long-term climate research. However, it is crucial to note that the GPCP and CMAP datasets offer a robust analysis of global precipitation by amalgamating various satellite datasets over both land and ocean, while incorporating gauge analysis specifically over land. Additionally, the monitoring product GPCC relies on meticulously quality-controlled data from approximately 7000 stations spanning from 1982 to the present day.

These three datasets collectively reveal near-neutral or slightly increasing trends in precipitation over global land during the dry season. Simultaneously, they indicate decreasing precipitation trends over the global ocean for GPCP and CMAP. This suggests that the contrast in precipitation changes between land and ocean has been accurately captured, leveraging both rain gauge and satellite datasets. In our study, we place particular emphasis on this contrast between oceanic and terrestrial precipitation during the dry season and endeavor to uncover the factors contributing to the observed slightly increasing or neutral dry season precipitation over land. While there may be some inconsistencies in terrestrial precipitation data, the overarching trend still demonstrates neutrality or slight increases, consistently highlighting divergent patterns between land and oceanic regions.

d. The synergy of reanalysis and observations

Hersbach et al. (2020) and Compo et al. (2011) illuminate the intricacies of data assimilation and the evolving nature of reanalysis datasets over time. In this study, we mainly wanted to look at how rainfall has changed over time and understand why these changes are happening. We used reanalysis data, which are not the same as direct observations, but these data blend a lot of different observations to give us good estimates of what the atmosphere is really like. We do acknowledge that adding new types of data into the mix, like satellite soil moisture data, can make things a bit complicated. This is especially true for aspects like evaporation, which rely a lot on the specifics of how the model is set up. We are aware that the ERA5 model also makes the adjustments for soil moisture, which could affect our findings on evaporation. Although these models are not perfect, they are still useful because they combine actual observations with model estimates to give us a fuller picture. We have to be careful when interpreting long-term changes based on these datasets.

This context is essential in understanding the analysis. In a related study, Jiao et al. (2021) highlighted a similar incongruence between ERA5 and observations, specifically over China. Their findings emphasized that the seasonal trends presented by ERA5 for the period 1979–2018 tend to be more subdued compared to actual observations, as evident in Fig. 7 of their work.

In a separate investigation, undertaken by Lavers et al. (2022), an evaluation was carried out to assess the accuracy of ERA5 precipitation data in comparison to observations obtained from meteorological stations. This evaluation was specifically conducted to ascertain the applicability of ERA5 data for climate monitoring activities. The assessment involved an examination of monthly precipitation patterns. The findings from this evaluation highlighted that ERA5 exhibits a higher level of skillfulness in representing precipitation patterns within the extratropical regions. However, the evaluation did not yield the same level of confidence in its representation of precipitation patterns within the tropics. This suggests that while ERA5 performs well in capturing extratropical precipitation trends, its accuracy and reliability in representing tropical precipitation patterns might be comparatively lower.

5. Summary

Previous research has indicated that both observations and model projections of future warming suggest that wet seasons are becoming wetter and dry seasons are becoming drier, particularly for ocean areas (Chou et al. 2013; Chou and Lan 2012). However, there has been limited discussion about the slight increases in precipitation observed over land during the dry season over the past four decades. This study used five observed precipitation datasets to demonstrate that the global averaged land precipitation during the dry season has slightly increased or remained neutral from 1979 to 2021, which is obviously in contrast to the ocean parts. To uncover the underlying causes of the observed rise in land precipitation, we delved into analyses of moist static energy. Our findings indicate that the areas experiencing augmented precipitation trends exhibit a slight trend toward a less stable atmosphere, while regions with declining dry season precipitation exhibit the opposite trend toward a more stable atmosphere. Furthermore, in regions with increasing precipitation, the decrease in relative humidity is less pronounced, resulting in higher relative humidity compared to areas experiencing decreasing precipitation. This plays a facilitating role in creating an atmosphere that is more conducive to rainfall.

The different trends in atmospheric conditions in the two areas are related to the differences in water vapor content trends below 400 hPa, which are slightly increasing in P′ > 0 areas and P′ < 0 in the dry season. The moisture budget analysis suggested that the dynamic component is the primary controlling mechanism for the precipitation trends in both regions. Moreover, the contrast in water vapor distribution between P′ < 0 and P′ > 0 may be related to the regional divergence changes in moisture fluxes, where there is more convergence in P′ > 0 regions and vice versa for P′ < 0 regions. The observed hotspots exhibiting pronounced increases (decreases) in dry season precipitation are believed to reflect regional circulation changes that have led to heightened (reduced) transport of water vapor from the ocean to inland regions. This increased (reduced) moisture flux has, in turn, resulted in greater (lesser) amounts of precipitation during the dry season. The nonhomogeneous distribution of local circulation changes may be due to anthropogenic factors such as global warming or land-use changes, or natural climate changes, which could be a worthy topic of future research.

Acknowledgments.

We thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions. This study was supported by the National Science and Technology Council Grant 110-2628-M-002-004MY4 to National Taiwan University, and Dr. Chao-An Chen was supported by the National Science and Technology Council Grant 110-2111-M-865-001-MY3. We also thank Mr. Jason Hsu for the fruitful discussion. The authors declare no competing financial interests.

Data availability statement.

The NCL codes for calculating the moisture budget and selecting dry season month have been uploaded to Zenodo (https://doi.org/10.5281/zenodo.7904605). The ERA5 reanalysis data used during this study are openly available from the ECMWF website https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5. NCEP reanalysis data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, from the website https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html. MERRA-2 reanalysis data were provided by the Goddard Earth Sciences Data and Information Services Center from the website https://disc.gsfc.nasa.gov/. The CRU data provided via the Centre for Environmental Data Analysis (CEDA) and also at the Climatic Research Unit (CRU) website at https://crudata.uea.ac.uk/cru/data/hrg/. The GPCP combined precipitation data were developed and computed by the NASA/Goddard Space Flight Center’s Mesoscale Atmospheric Processes Laboratory as a contribution to the GEWEX Global Precipitation Climatology Project. The CMAP data are provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, United States, from the website at http://www.cdc.noaa.gov/. GPCC precipitation data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at https://www.esrl.noaa.gov/psd/. UDel precipitation data were provided by the University of Delaware from the website at https://www.esrl.noaa.gov/psd/.

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