**Diurnal Variations of Di**ff**erent Cloud Types and the Relationship between the Diurnal Variations of Clouds and Precipitation in Central and East China**

#### **Cuicui Gao 1,2, Yunying Li 1,\* and Haowei Chen <sup>2</sup>**


Received: 19 April 2019; Accepted: 25 May 2019; Published: 3 June 2019

**Abstract:** In this paper, the diurnal variations of various clouds are analyzed using hourly cloud observations at weather stations in China from 1985 to 2011. In combination with merged hourly precipitation data, the relationship between the diurnal variations of clouds and precipitation in the summers from 2008 to 2011 are studied. The results show that the occurrence frequencies of total cloud and various cloud types exhibit significant diurnal variations. The diurnal variations of the occurrence frequencies of altocumulus and stratocumulus show a bimodal pattern, with peaks appearing in the early morning and late afternoon. The early morning peaks of altocumulus and stratocumulus appear earlier in the summer than in the other seasons, while the late afternoon maxima show an opposite trend. The occurrence frequency of nimbostratus peaks in the morning between 07 and 12 LST (local solar time), and the peak value lags 2 to 3 h from west to east along the Yangtze River valley; meanwhile, the diurnal variation shows no clear differences caused by changes in the latitude or seasons. Cumulus shows an afternoon (14 LST) maximum, while cumulonimbus peaks in the late afternoon during 16–20 LST, and both of them present a great diurnal range. Cirrus usually reaches its peak at 17–18 LST, and it differs by 1 to 2 h with a change in the latitude. The results of the study first show that the diurnal variations of precipitation among different regions are dominated by different clouds. The upper reaches of the Yangtze River valley present a midnight precipitation maximum that is mainly dominated by cumulonimbus. For the middle reaches of the Yangtze River valley impacted by nimbostratus, the precipitation peaks in the early morning. In South and Northeast China, the precipitation peaks in the afternoon and is determined by the diurnal variations of convective clouds. In the region between the Yangtze River valley and Yellow River valley, the precipitation peaks in the early morning and afternoon; the early morning peak is mainly determined by stratiform clouds, while the afternoon peak is closely related to convective clouds.

**Keywords:** clouds; precipitation; diurnal variation

#### **1. Introduction**

Diurnal variations are the most basic period of change in the Earth's climate system, the most important driving force of which is solar radiation. Various meteorological parameters, such as the temperature, wind, pressure and precipitation, all show significant diurnal variations. Rutledge noted that stratiform and convective precipitation are produced by different cloud microphysical processes [1]. The formation mechanism of precipitation is complex, but it ultimately originates from clouds. Clouds are the external manifestation of dynamic and thermodynamic processes in the atmosphere, and there are many differences in the nature of precipitation sourced from different types of clouds. The diurnal variation of clouds reflects the changes in the internal movements throughout the atmosphere.

However, the diurnal cycle of clouds also plays a feedback role in the movements of the atmosphere in addition to the radiation process and the water cycle [2]. Furthermore, the diurnal variation of clouds also reflects the atmospheric stability and changes in the weather. Therefore, studying the diurnal variation of clouds is of significance for understanding the diurnal cycle of the internal movements throughout the atmosphere. In addition, understanding the relationship between the diurnal variations of clouds and precipitation is beneficial to provide a physical basis and observe relevant facts for cloud parameterization schemes in weather and climate models, which is helpful for improving the ability to simulate weather and climate models.

Precipitation exhibits significant diurnal variation characteristics. Consequently, many scholars have carried out a great deal of research [3–9]. Yu et al. noted that the summer precipitation over contiguous China has large diurnal variations with considerable regional features [10]. Over southern inland China and northeastern China, the summer precipitation peaks in the late afternoon, while the precipitation in some regions peaks between midnight and the early hours of the morning. Zhou et al. verified the above results by comparing satellite to rain gauge observations, showing that the diurnal phase of rainfall frequency and intensity were similar to those of the rainfall amount in southern China [11]. A study by Li et al. showed the clear differences in the diurnal cycle of precipitation between southwestern China and southeastern China [12]. The diurnal cycle of the annual mean precipitation in southwestern China tends to reach a maximum either at midnight or during the early morning, while the precipitation in eastern China peaks in the late afternoon. Yuan et al. analyzed the sub-seasonal characteristics of the diurnal variation of summer monsoon rainfall over eastern central China and found that the early-morning diurnal peaks experience sub-seasonal movements similar to those of the monsoon rain belt [13]. The aforementioned scholars have obtained many meaningful conclusions about the diurnal variation of precipitation. Moreover, since precipitation originates from clouds, the diurnal variation of precipitation is closely related to the diurnal variations of different types of clouds, and therefore, studying the relationships between the diurnal variations of clouds and precipitation could be helpful for further understanding the mechanism of precipitation.

William et al. presents observational evidence in support of the existence of a large diurnal cycle of oceanic, tropical, deep cumulus convections [14]. Zheng et al. used satellite infrared temperature of black body (TBB) data to explore the climatological characteristics of deep convection over southern China and the adjacent seas during the June-August periods of 1966–2007 [15]. They found that sea-land and mountain-valley breezes accounted for the propagation of deep convection from sea to land in the afternoon and from land to sea after midnight. Based on hourly infrared brightness temperature data acquired by the FY-2C satellite to classify clouds into cold clouds, middle clouds and warm clouds according to the cloud top temperature, Chen et al. analyzed the diurnal variations of three types of clouds in southern China during the summer periods from 2005 to 2008 and the seasonal changes in the diurnal variations of those clouds [16]. Fujinami et al. studied the diurnal variations of high clouds and precipitation over the Tibetan Plateau, where observation stations are sparse, using satellite observations. He noted that the cloud cover frequency for high clouds increased in the afternoon along the ridges and reached a maximum near 18 LST, after which high cloud cover frequencies moved over the valley and persisted until early morning [17]. In addition, a similar evolution occurred in the frequency of rainfall. Zhao et al. based on MODIS observations, showed the statistical characteristics of cloud properties, along with the difference between morning and afternoon [18]. These conclusions have promoted the understanding of the diurnal variations of clouds; however, the corresponding research is relatively fragmented, and the relationship between the diurnal variations of clouds and precipitation has not yet been established. Therefore, this paper aims to analyze the climatic characteristics of the diurnal changes of clouds over China in addition to the relationship between the diurnal variations of clouds and precipitation to deepen the existing understanding of the correlation between clouds and precipitation and to provide the basis for a simulation of the diurnal variations of clouds and precipitation in weather and climate models.

#### **2. Data and Analysis Method**

#### *2.1. Data*

In this study, hourly cloud data during 1985–2011 are acquired from 114 surface observation stations after a quality-control procedure is performed. Since the distribution of stations to the west of 105◦ E is sparse, this study mainly analyzes the diurnal variations of clouds over eastern and central China. The dataset includes the information of total cloud (TC), and 10 cloud genera (cirrus (Ci), cirrocumulus (Cc), cirrostratus (Cs), altocumulus (Ac), altostratus (As), nimbostratus (Ns), stratocumulus (Sc), stratus (St), cumulus (Cu), and cumulonimbus (Cb)). Due to the light intensity and visual reasons, artificial observations on the ground will produce some random observation errors, however, many years of climatological normal results can significantly reduce the instabilities of artificial observations. In addition, the reliability of the dataset has been tested and verified [19]. From the perspective of surface observations, the diurnal variation of low clouds is relatively accurate. High clouds and middle clouds are sometimes blocked by low clouds, and the article therefore analyzes the diurnal variation of a certain type of high (middle) cloud when it appears.

Hourly merged precipitation data during 2008–2011 were obtained from the National Meteorological Information Center of the China Meteorological Administration with a spatial resolution of 0.1◦ × 0.1◦. These hourly precipitation data were merged with more than 30,000 automatic weather stations over China in addition to global Climate Prediction Center morphing technique (CMORPH) precipitation estimates with a temporal interval of 30 min and a resolution of 8 km provided by the American Climate Prediction Center. The merged precipitation product effectively combines the advantages of surface precipitation observations with satellite retrieved precipitation products. The overall error of the product is less than 10%, which is better than the same type of product worldwide [20]. The quality of the merged precipitation products in the sparse station areas still needs improvement. However, the stations in eastern and central China studied in this paper are densely and effectively distributed, and the precipitation values are accurate since station data were utilized as the main data source in the fusion process. Since 56.5 percent of the precipitation falls during the summertime throughout most of China [21], this paper mainly analyzes the relationship between the diurnal variations of clouds and precipitation during the summer.

The diurnal variation of clouds is the average of the data from the stations from 1985 to 2011. Since the precipitation data range from 2008 to 2011, to match the precipitation data, the cloud data are truncated from 2008 to 2011, when the relationship between the diurnal variations of clouds and precipitation is analyzed.

#### *2.2. Analysis Method*

The diurnal variations of the meteorological parameters are the changes in the meteorological parameters with the local solar time (LST) [5,22]. Therefore, statistical work on the diurnal variations of clouds and precipitation should be conducted according to the local solar time. The time system for the hourly cloud data used in this paper corresponds to China Standard Time (CST), and the method used to convert CST into LST is as follows:

$$T1 = \left[\frac{\text{lon} + 7.5}{15}\right] - 8 + \text{T} \tag{1}$$

$$\text{LST} = \begin{cases} T1 & 0 < T1 \le 24 \\ 24 + T1 & T1 \le 0 \\ T1 - 24 & T1 > 24 \end{cases} \tag{2}$$

where lon represents the longitude of the station, and the unit of lon is degree, T denotes CST, LST is the local solar time, and [] indicates an operation to change the number into an integer.

The time system for the precipitation data used in this paper corresponds to Greenwich Mean Time (GMT), and the method used to convert GMT into LST is similar to CST, and it just don't need to subtract the time zone in the first step. The time used hereafter in this study is LST.

The occurrence frequency of a cloud is defined as F = (reccld/rec) × 100%, where F represents the occurrence frequency of a cloud at a certain time, reccld represents the number of cloud occurrences at a certain time within the analysis period, rec is the number of observations at a certain time, and the definition of the occurrence frequency is suitable for all types of cloud, including TC. The maximum occurrence frequency of a cloud from 01 to 24 LST in a day is the daily peak. The amplitude of the diurnal variation of clouds reflects the magnitude of the maximum and minimum occurrence frequency of cloud in 24 h and is defined as A = cldmax/cldavg, where A is the amplitude, cldmax is the daily peak of the cloud occurrence frequency, and cldavg is the daily occurrence frequency of the cloud. When analyzing the diurnal variation of clouds in different seasons, to eliminate the differences in the occurrence frequencies of cloud among the different seasons, this paper calculates the anomalies in the occurrence frequencies of clouds in each month from 01 to 24 LST.

#### **3. Climatic Characteristic of Diurnal Variation of Clouds**

#### *3.1. Diurnal Variation of Di*ff*erent Types of Clouds*

To obtain an overall understanding of the diurnal variations of the total clouds (TC) and of the various types of clouds over central and eastern China, this paper first analyzes the diurnal variations of the average occurrence frequencies of the TC and various clouds at 114 stations over central and eastern China. The diurnal variation of the frequency of TC shows a bimodal pattern; the main peak appears in the late afternoon, and the second peak appears in the early morning (Figure 1a). During the day, the occurrence frequency of the TC is significantly greater than that of nocturnal clouds. After 04 LST, the occurrence frequency of the TC increases rapidly and reaches a peak at 07 LST. The frequency of the TC changes slightly during 07–09 LST, and it begins to increase again after 09 LST and reaches the main peak at 14–15 LST. Ac also shows a diurnal variation with a bimodal pattern with peaks at 07 LST and 18 LST, and the morning peak is larger. As occurs at a frequency of less than 3.5% but at a relatively high frequency in the afternoon.

**Figure 1.** The diurnal variation of the occurrence frequencies (%) of various cloud types in central and east China during 1981–2011: (**a**) TC, Ac, As, (**b**) Ns, Sc, St, (**c**) Cu, Cb, Ci(the horizontal axis represents the local solar time, and the vertical axis represents the occurrence frequency of clouds).

The diurnal cycle of the occurrence frequency of Ns is unimodal, and it occurs at approximately 08 LST. Ns is a component of frontal clouds, which are mainly formed as deep, moist air rises along the front surface; the relative humidity of the air is larger in the morning and is saturated more easily and condensed, and thus, Ns appears more frequently in the early morning. The diurnal variation of St is consistent with that of Ns, and its diurnal peak appears at 08 LST. St, whose formation mechanism is the same as that of fog, is generally formed by fog rising. Fog usually appears during the night until

08 LST, and it especially occurs throughout most of the morning; St also shows such a pattern in the morning. The diurnal variation of the occurrence frequency of Sc presents a bimodal pattern with a main diurnal peak at 06 LST and a second peak at 17 LST. As a typical boundary layer cloud, the diurnal variation of Sc reflects the diurnal variation of the boundary layer. The low temperature in the boundary layer and the stable atmospheric stratification in the morning are beneficial for water vapor below the top of the boundary layer to accumulate and form Sc. When the atmosphere stability deteriorates, the conditions are no longer favorable to the formation of stratiform clouds and the occurrence of Sc. As the solar radiation weakens in the evening and the atmosphere stratification tends to become stable, the frequency of Sc starts to increase again and reaches the second peak at approximately 19 LST.

The diurnal variation of Cu is also unimodal (Figure 1c). During the daytime, as the solar radiation gradually enhances, the atmosphere temperature around and above the ground surface gradually rises while the instability of the atmospheric stratification increases, and thus, the occurrence frequency of Cu begins to increase beginning in the early morning and reaches a peak at 14 LST, after which it decreases. The diurnal variation of the occurrence frequency of Cb shows a unimodal pattern as well. The occurrence frequency of Cb is high at 16–20 LST, and its main diurnal peak appears at 16–17 LST, which is 4–6 h behind the time when Cu reaches its diurnal peak, indicating that it takes some time for shallow convection clouds to develop into deep clouds. The frequency of Ci increases from 04 to 05 LST continuously, and it reaches a maximum at 17 LST, after which it rapidly decreases after 18 LST. Sometimes, Ci acts as an anvil to the development of Cb to a mature stage, and thus, there is a lag in the time required for Cu, Cb to Ci to reach their diurnal peaks.

Figure 2 shows the times of the diurnal peaks of different clouds and the spatial distribution of the amplitudes of their diurnal variations over central and eastern China. The figure shows the time when the occurrence frequency peaks at each station in the form of a clock, and the number indicates the amplitude of the diurnal variation of the cloud occurrence frequency. Figure 2a shows that the diurnal peak of the TC appears from the afternoon to the late afternoon, but it appears in the early morning at some stations. For example, at most of the stations to the north of 32◦ N in China, the diurnal peak appears at 15–18 LST; meanwhile, over the Liaodong Peninsula and Shandong Peninsula, the diurnal peak appears a little earlier at 13–14 LST. However, to the south of 32◦ N, the diurnal peak appears in two periods at 14–18 LST and 06–07 LST. For instance, the diurnal peak of the TC in the southeastern coastal areas of China mainly occurs in the early morning at 06–07 LST. The amplitude of the occurrence frequency of the TC is large in northern China (approximately 1.2–1.3), while that at most stations in southern China is approximately 1.1, indicating that the diurnal variations of the occurrence frequencies of clouds in northern China are greater than those in southern China.

**Figure 2.** *Cont*.

**Figure 2.** Spatial distributions of the phases and amplitudes of the diurnal cycles of various clouds: (**a**) TC, (**b**) Ci, (**c**) Ns, (**d**) Sc, (**e**) Cu, and (**f**) Cb. The directions of the vectors denote the local solar time (LST) of the maximum cloud occurrence frequency (phase clock in (**b**)), while the length of the vectors represent the amplitude of the diurnal cycle, the reference arrow represents the amplitude of diurnal cycle is 1.0.

According to this study, the diurnal peak of Ci appears at 17–18 LST consistently at each station (Figure 2b), and the diurnal variation amplitude is larger in South China and in the Sichuan Basin.

The diurnal peak of Ns occurs in the morning (08–09 LST) (Figure 2c), but those of some stations in Shanxi Province, western Henan Province, and Hubei Province and in the Sichuan basin occur around noon (11–14 LST). The Meiyu front near the Yangtze River valley is also a frequent area of Ns formation, yet the diurnal variation amplitude of Ns in this area is small. Sc is the cloud with the highest occurrence frequency over China. Its diurnal peak over most stations occurs at 06–07 LST (Figure 2d), but those in western Inner Mongolia and North China usually occur at 18–19 LST.

The diurnal peak of Cu over all stations appears in the afternoon (12–14 LST) (Figure 2e). The diurnal variation of Cu in most areas throughout China is large, and its amplitudes are basically above 2.9, while the diurnal variation of Cu in South China is relatively small with amplitudes between 1.9 and 3.1. The frequency of Cb usually reaches a peak value at 16–20 LST (Figure 2f); however, at very few stations, the peak occurs at 02–04 LST. In addition, the diurnal variation of Cb is large with the second-largest amplitude (approximately 1.5 to 2.7) following that of Cu.

The analysis above shows that there are regional differences among the diurnal peaks of Ac and Sc that are mainly due to the frequent occurrences of both cloud types in the early morning and evening with different peak times at different stations. As Ac and Sc occur more frequently, they have a great impact on the diurnal variation of the TC occurrence frequency, and thus, the TC shows a similar diurnal changing pattern with these two types of clouds. The diurnal peak of Ns mainly appears in the morning (07–12 LST), and the time difference in the diurnal peaks between different stations does not exceed 5 h. The diurnal peak of Ci usually appears in the evening (17–18 LST) while that of Cu appears in the afternoon but shows no clear regional difference. Finally, the diurnal peak of Cb mainly appears at 16–20 LST with slight differences among different regions. The diurnal variations of Cu and Cb are both relatively enormous.

#### *3.2. Diurnal Variation Cycles of Clouds with the Latitude and Longitude*

Figure 3 shows the diurnal variations of the TC and of various cloud types with the latitude; the zonal average of 110–120◦ E represents the occurrence frequency of cloud types at each latitude. Figure 3a illustrates that the diurnal peak of the TC to the south of 20◦ N appears in the afternoon (12–15 LST), which is closely related to the diurnal variation of convective clouds. Meanwhile, the diurnal peak of the TC at 20–26◦ N (i.e., southern China) appears in two periods during the early morning (06–08 LST) and afternoon (11–16 LST). This is probably due to the diurnal peaks of Ac and Sc in these areas that occur in the early morning and evening and have a great impact on the diurnal variation of the TC occurrence frequency. The diurnal peak of the TC at 26–36◦ N appears mainly in the afternoon. The occurrence frequency of the TC to the north of 36◦ N increases with the latitude with a maximum at 16 LST.

**Figure 3.** Diurnal variation cycles of occurrence frequency (%) of clouds with the latitude averaged over 110–120◦ E: (**a**) TC, (**b**) Ci, (**c**) Ac, (**d**) Ns, (**e**) Sc, and (**f**) Cb (the horizontal axis is the local solar time in hours, and the vertical axis represents the latitude).

The occurrence frequency of Ci at 20–32◦ N over China is small, but the occurrence frequencies over Hainan Province and to the north of 32◦ N are large, and the frequency of the latter area increases with the latitude (Figure 3b). Moreover, there is a slight difference in the diurnal peak of Ci between the two areas. The occurrence frequency of Ci over Hainan Province is large (approximately 35%), and the peak appears at 06 LST and 18 LST. The diurnal peak of Ci to the north of 32◦ N occurs at 16–17 LST, and the occurrence frequency of Ci near 45◦ N reaches 40% at 16 LST. The occurrence frequency of Ac increases with the latitude from south to north (Figure 3c); it reaches a maximum at 28–36◦ N and then decreases. At 28–36◦ N, the diurnal peak of Ac appears at 07 LST and 18 LST, while in other areas the diurnal peak of Ac appears at approximately 08 LST, indicating that its maximum daily peak appears at a difference of 1 to 2 h with a change in the latitude.

Figure 3d shows a high occurrence frequency of Ns within 26–29◦ N. After 04 LST, the high value of the frequency of Ns begins to expand to the south and to the north. In addition, a frequency of 10% expands to the south to 22◦ N at 08 LST and to the north to 32◦ N at 09 LST. After 09 LST, the frequency of Ns begins to shrink, as the maximum frequency of Ns occurs at 09 LST. The diurnal peak of Ns does not change significantly with the latitude. The diurnal peak of Sc occurs in two periods during the early morning (approximately 06 LST) and the late afternoon (18–19 LST) (Figure 3e). The diurnal peak of Sc over Hainan Province appears at 05 LST and 18 LST while that at 20–28◦ N over China occurs at 06 LST and 19 LST. Moreover, the diurnal peak of Sc at 18–25◦ N lags behind by approximately 1 h. The diurnal peak of Cb over Hainan Province is reached at 09 LST while that of Cb to the north of 20◦ N mainly occurs in the period of 16–20 LST, and its appearance time lags behind with the latitude (Figure 3f). There is no difference in the diurnal peak of Cu with the latitude, as it consistently occurs at 13–14 LST (figure omitted).

The diurnal variation cycles of clouds with the longitude are not as clear as that with the latitude. Ns shows clear zonal difference over the Yangtze River valley and its south side, where the occurrence frequency of Ns is the largest. In the upper reaches of the Yangtze River, the diurnal peak of Ns occurs at 06–09 LST, while the diurnal peaks over the middle and lower reaches of the Yangtze River appear at 07–13 LST (Figure 4a). The occurrence frequency of Cu is the largest in south China. And the diurnal variation of the occurrence frequency of Cu does not change with the longitude (Figure 4b) in south China. The peak occurrence frequency appears in the afternoon (13–14 LST); however, at 22–26◦ N, the frequency of Cu decreases clearly from west to east. The diurnal variations of the TC and the other types of clouds show no significant change with the longitude.

**Figure 4.** Diurnal variation cycles of clouds with the longitude: (**a**) Ns (averaged over 26–30◦ N) and (**b**) Cu (averaged over 22–26◦ N) (the horizontal axis represents the longitude, and the vertical axis is the local solar time in hours).

#### *3.3. Diurnal Variation Cycles of Clouds with the Season*

The diurnal peak of the TC over China during the spring occurs at approximately 16 LST (Figure 5a). In addition, there is a clear diurnal variation of the TC in the summer and autumn (i.e., from June to November). The maximum occurrence frequency occurs at 14 LST while the minimum occurs at 23 LST, and the difference between them reaches 28%. In addition, the amplitude of the diurnal

variation of the TC in the winter is small; at 13–15 LST, however, the occurrence frequency is relatively large. In contrast, the occurrence frequency of the TC during the summer reaches a peak time 1–2 h earlier than that during the spring, which may occur because the mean temperature increases more rapidly in the summer than in the spring, and thus, the conditions that are unstable for cloud formation are reached earlier.

**Figure 5.** Seasonal variations in the diurnal cycle of anomalous values of the occurrence frequencies of clouds: (**a**) TC, (**b**) Ci, (**c**) Ac, (**d**) Ns, (**e**) Sc, (**f**) Cu (the horizontal axis is the local solar time in hours, and the vertical axis represents the month).

The diurnal peak of Ci also changes with the season (Figure 5b). Ci reaches its maximum at 16–17 LST in December and at 18–19 LST in July, thereby lagging 1–2 h between the winter and summer. The changes in the peak times of Ac are the most clear from June to September (Figure 5c). The peak times of the two peaks appear from early winter to summer, while the early morning peak appears earlier and the evening peak appears later in the summertime. The diurnal peak times of Ns and Cu do not change with the seasons (Figure 5d,f), but both of the amplitudes of their diurnal variations are relatively large in the summer. The diurnal variation of Sc with the seasons is somewhat similar to that of Ac (Figure 5e), but the minimum occurrence frequency always appears at 14 LST and does not change with the seasons. The occurrence frequency of Cb in the winter is very small, and its seasonal variation is also not clear and therefore is not discussed here.

#### **4. The Relationship between the Diurnal Variations of Clouds and Precipitation**

#### *4.1. Diurnal Variation of Precipitation*

The study of Yu et al. showed that the regional differences in the diurnal variation of summer precipitation is significant across the Chinese mainland [10]. Therefore, in order to analyze the relationship between the diurnal variations of clouds and precipitation, the following analysis is based on the regional division of central and eastern China into five sub-regions (Table 1) by Yu et al. , who studied the diurnal variation of precipitation.


**Table 1.** The information of five regions in eastern China.

The reliability of the hourly merged precipitation data has been verified by Shen et al. [23]. To compare the error of the hourly merged precipitation data and rain-gauge data, we first analyzed the diurnal variation of the precipitation in each of the five regions by using the hourly merged precipitation data from 2008 to 2011 in summer (June-August). The diurnal variation of precipitation in each of the five regions is the average of grid data of each regions. Figure 6a shows that the maximum precipitation in the upper reaches of the Yangtze River occurs at midnight (24–02 LST), while the minimum precipitation falls at noon (12 LST). Meanwhile, the precipitation in the middle reaches of the Yangtze River reaches a diurnal peak in the early morning (06–07 LST) and a sub-peak in the afternoon (14–16 LST) (Figure 6b). The diurnal peaks of precipitation in South and Northeast China appear in the afternoon (16–17 LST) (Figure 6c,d). Although the peak precipitation between the Yangtze River valley and the Yellow River valley (region 5) differs among different stations (figure omitted), the diurnal variation of precipitation in this region is generally bimodal with peaks in the early morning (06–08 LST) and afternoon (16–18 LST). By comparing our results with the diurnal variation characteristics of the precipitation in the five regions from 1991 to 2004 reported by Yu et al. (Figure 2 in Yu's article), it is found that the hourly merged precipitation data can effectively reflect the diurnal variation of the precipitation in each region. However, the amounts of precipitation in these data are smaller than those observed by the stations. Therefore, in the following discussion, we mainly analyze the corresponding relationship between the diurnal cycles of clouds and precipitation regardless of the diurnal variation of the precipitation amount.

**Figure 6.** Diurnal variation of the 2008–2011 mean summer precipitation averaged over the five regions in Table 1 (the horizontal axis corresponds to the local solar time, units: mm/h).

#### *4.2. The Relationship between the Diurnal Variations of Clouds and Precipitation*

The probability occurrence of Ci and Ac with precipitation is relatively small, and thus, they are classified as non-precipitation clouds [19]. Therefore, this paper mainly discusses the correspondence between the diurnal variations of Ns, Sc, Cu and Cb and that of precipitation. Table 2 shows the correlation coefficients between the diurnal variations of various clouds and precipitation, where P\_Ns represents the correlation coefficient between the diurnal variation of the occurrence frequency of Ns and that of precipitation, and it has a similar meaning for P\_Sc, P\_Cu and P\_Cb, while the clouds are different.


**Table 2.** Correlation coefficient between the diurnal cycle of precipitation and the occurrence frequency of clouds in summer.

Note: Bold font indicates a 0.05 significance level.

Figure 7a1 shows that the diurnal variation of Cb is in good agreement with that of precipitation in the upper reaches of the Yangtze River; both reach a peak around midnight with a significant positive correlation coefficient of 0.90 (Table 2). While there is a clear negative correlation between the diurnal variations of Cu and precipitation, there is also a consistent correlation between the diurnal variations of Sc and precipitation (Figure 7a2), but the diurnal peak of Sc occurs slightly later than that of precipitation. This shows that the diurnal variation of precipitation in the upper reaches of the Yangtze River relies upon the diurnal variations of Cb and Sc. Moreover, the correspondence between the diurnal variations of Cb and precipitation is better, and Cb may evolve into Sc after precipitation falls; thus, the diurnal peak of Sc lags behind that of precipitation by approximately 2 h.

**Figure 7.** Diurnal variations of the standardized occurrence frequencies of Cu, Cb and precipitation (in **a1**–**e1**) and of the standardized occurrence frequencies of Ns, Sc and precipitation (in **a2**–**e2**) (the black solid line corresponds to precipitation, while the blue dotted line and red dashed line represent the occurrence frequencies of Cu and Cb, respectively, in **a1**–**e1**; meanwhile, the blue dotted line and red dashed line represent the occurrence frequencies of Ns and Sc, respectively in **a2**–**e2**).

The precipitation in the middle reaches of the Yangtze River mainly occurs in the early morning (Figure 7b1). There is a sub-peak at approximately 15 LST, the occurrence of which corresponds to the sub-peaks of both Cu and Cb, indicating that the afternoon rainfall in the middle reaches of the Yangtze River mainly originates from Cu and Cb. Figure 7b2 shows that the corresponding relationship between the diurnal variations of precipitation and stratiform clouds in the middle reaches of the Yangtze River is good, and there is a notable positive correlation between precipitation and Ns with a correlation coefficient of 0.51. These results show that the peak of early morning rainfall in the middle reaches of the Yangtze River is mainly caused by stratiform clouds, while the sub-peak of afternoon rainfall mainly depends upon Cu and Cb.

The diurnal peaks of Cu, precipitation and Cb appear sequentially over southern China (Figure 7c1). The peak frequency of Cu occurs at 12 LST, while those of precipitation and Cb occur at 16 LST and 20 LST, respectively. In addition, the occurrence frequency of Cu decreases during 12–16 LST, but those of precipitation and Cb rapidly increase during this period, implying that Cu begins to produce precipitation during this period and that a part of Cu develops vigorously into Cb under the favorable conditions. However, the corresponding relationship between the diurnal variations of stratiform clouds and precipitation is poor over South China (Figure 7c2). In conclusion, both Cu and Cb over southern China show a good correspondence with the diurnal variation of precipitation, indicating that the diurnal variation of precipitation in southern China is mainly attributed to the diurnal variations of convective clouds.

There is a significant positive correlation between the diurnal cycles of Cu and precipitation in Northeast China with a correlation coefficient of 0.55. Cb shows a better correspondence with the diurnal variation of precipitation (Figure 7d1), with a significant positive correlation of 0.88. For example, the diurnal peaks of Cb and precipitation are both almost synchronous at 09–16 LST, although the diurnal peak of Cb that occurs during 01–09 LST is a little earlier than that of precipitation, and the occurrence frequency of Cb peaks one hour later than that of precipitation. In addition, the correspondence between the diurnal variations of stratiform clouds and precipitation is also poor in these areas (Figure 7d2). This shows that the diurnal variation of precipitation in Northeast China is primarily dominated by convective clouds, especially Cb.

There are two peaks in the diurnal variation of precipitation in the region between the Yangtze River and the Yellow River (region 5). In the afternoon, Cu, precipitation and Cb reach their peaks sequentially (Figure 7e1). Cu peaks at 14 LST, while the occurrence frequencies of precipitation and Cb increase rapidly after 12 LST. Cb and precipitation both peak at 17 LST, indicating that the afternoon peak of precipitation is determined by convective clouds. Figure 7e2 shows that the diurnal peaks of Sc and Ns occur at 05 LST and 09 LST, respectively, while the precipitation peaks at 07 LST, meaning that the morning precipitation peak in the area is dominated by stratiform clouds. Therefore, the results show that the diurnal variation of precipitation in the area is comparatively complicated and is closely related to both convective and stratiform clouds. The morning precipitation is closely related to stratiform clouds, while the afternoon precipitation is dominated by convective clouds.

All of the above analysis shows that the precipitation peak in central and eastern China usually occurs in the early morning or afternoon. The precipitation peak in early morning is dominated by stratiform clouds, while the peak in the afternoon is dominated by convective clouds.

#### **5. Discussions and Conclusions**

Based on hourly cloud observations at weather stations in combination with merged hourly precipitation data, the diurnal climatic features of different types of clouds in central and eastern China are revealed and the relationship between the diurnal variations of clouds and precipitation are analyzed. The primary conclusions are as follows:

The TC and the occurrence frequencies of all types of clouds show significant diurnal variations. The diurnal variation of the TC shows a bimodal pattern with a main peak in the late afternoon. Ac, Sc and Ci contribute the most to the diurnal variation of the TC. The diurnal cycles of the occurrence frequencies of Ac and Sc also show a bimodal pattern with peaks appearing in the early morning and late afternoon. The occurrence frequency of nimbostratus peaks in the morning between 07 and 12 LST, and the daily difference is weaker. The diurnal peak of Ci usually appears at 17–18 LST in the late

afternoon, Cu peaks in the afternoon, and Cb peaks in the late afternoon during 16–20 LST, and there are slight differences among different regions. In addition, the diurnal variation amplitudes of Cu and Cb are larger, indicating that they both have significant diurnal variations.

The diurnal variations of clouds vary with both the latitude and the longitude. There is a slight difference in the time when the occurrence frequency of the TC reaches its peak value at different latitudes, while there is a difference of 1–2 h with a change in the latitude for both Ci and Ac. The peak times of Ns, Sc, Cu and Cb do not change with the latitude. In addition, the peak appearance time of the occurrence frequency of Ns in the Yangtze River valley lags by 1–2 h from west to east.

The diurnal features of clouds change with the seasons. All of the types of clouds show the largest diurnal amplitudes in the summer and autumn, and the time when the TC and Ci reach their maximum values tend to lag by 1–2 h from the spring to the summer. Ac and Sc present bimodal patterns with early morning peaks that appear earlier in the summer, while their late afternoon peaks appear later in the summer than in the other seasons. The maximum peak appearance times of Ns and Cu do not change with the seasons.

The diurnal variations of precipitation among the different regions are dominated by convective or stratiform clouds. The upper reaches of the Yangtze River valley present a midnight precipitation maximum that is mainly dominated by Cb. Both precipitation and Cb reach a peak around midnight with a significant positive correlation coefficient of 0.90. In other regions, the precipitation usually peaks in the early morning or in the afternoon, and there is a notable positive correlation between the early morning peak of precipitation and stratiform clouds, while the afternoon peak of precipitation shows positive relationship with convective clouds. The diurnal peak in the Yangtze River valley and the region between the Yangtze River valley and the Yellow River valley is mainly dominated by stratiform clouds, while it is mainly determined by convective clouds in South and Northeast China. The peak appearing in the afternoon over the Yangtze River valley is also dominated by convective clouds.

Nesbitt and Zipser suggested that the nocturnal rain is often caused by meso-scale convective systems (MCS) rather than isolated convection, and the MCSs are the strongest systems after midnight [24]. Lin et al. indicated that the nocturnal maximum rainfall is enhanced by instability due to nocturnal radiative cooling at cloud top [25]. This explains that the midnight precipitation maximum is mainly dominated by Cb in the upper reaches of the Yangtze River valley. The afternoon peaks of convective clouds and precipitation can be explained by the highest surface air temperature and the strongest instability in the lower troposphere occurring in the afternoon, which are beneficial to the trigger of the convection [22].

The study points out the relationship of diurnal variations of clouds and precipitation among different regions for the first time. However, the research on the physical mechanism of the diurnal variations of clouds and precipitation is not deep. We will study the diurnal variation of cloud microphysical characteristics, and analyze the influence of terrain, atmospheric stability and atmospheric circulation on the diurnal variation of clouds and precipitation in the next step. This is helpful for understanding the mechanism of clouds and precipitation, and for improving the ability to simulate weather and climate models.

**Author Contributions:** Conceptualization, C.G.; Data curation, C.G. and H.C.; Formal analysis, C.G.; Funding acquisition, Y.L.; Project administration, Y.L.; Validation, H.C.; Visualization, C.G. and H.C.; Writing – original draft, C.G.; Writing – review and editing, Y.L.

**Funding:** This research was funded by the National Key Research and Development Program of China, Grant /Award Number 2018YFC1507604; National Natural Science Foundation of China, Grant/Award Number 41475069.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Impacts of Green Vegetation Fraction Derivation Methods on Regional Climate Simulations**

#### **Jose Manuel Jiménez-Gutiérrez 1, Francisco Valero 1, Sonia Jerez <sup>2</sup> and Juan Pedro Montávez 2,\***


Received: 8 March 2019; Accepted: 15 May 2019; Published: 21 May 2019

**Abstract:** The representation of vegetation in land surface models (LSM) is crucial for modeling atmospheric processes in regional climate models (RCMs). Vegetation is characterized by the green fractional vegetation cover (FVC) and/or the leaf area index (LAI) that are obtained from nearest difference vegetation index (NDVI) data. Most regional climate models use a constant FVC for each month and grid cell. In this work, three FVC datasets have been constructed using three methods: ZENG, WETZEL and GUTMAN. These datasets have been implemented in a RCM to explore, through sensitivity experiments over the Iberian Peninsula (IP), the effects of the differences among the FVC data-sets on the near surface temperature (T2m). Firstly, we noted that the selection of the NDVI database is of crucial importance, because there are important bias in mean and variability among them. The comparison between the three methods extracted from the same NDVI database, the global inventory modeling and mapping studies (GIMMS), reveals important differences reaching up to 12% in spatial average and and 35% locally. Such differences depend on the FVC magnitude and type of biome. The methods that use the frequency distribution of NDVI (ZENG and GUTMAN) are more similar, and the differences mainly depends on the land type. The comparison of the RCM experiments exhibits a not negligible effect of the FVC uncertainty on the monthly T2m values. Differences of 30% in FVC can produce bias of 1 ◦C in monthly T2m, although they depend on the time of the year. Therefore, the selection of a certain FVC dataset will introduce bias in T2m and will affect the annual cycle. On the other hand, fixing a FVC database, the use of synchronized FVC instead of climatological values produces differences up to 1 ◦C, that will modify the T2m interannual variability.

**Keywords:** fraction vegetation cover; regional climate model; near surface temperature

#### **1. Introduction**

Land surface features play a very important role in modulating surface-atmosphere interactions. This is an overlooked issue in climate simulations [1] and short-term weather forecasting [2]. Surface components like soil moisture, albedo, emissivity, surface roughness, vegetation type and amount are fundamental since they control the energy partition at surface [3]. Therefore, the representation of all these variables in land surface models (LSM) is crucial for modeling atmospheric processes.

Atmosphere and vegetation interact in different ways, controlling evapotranspiration, moisture availability, momentum transfer, partitioning radiation, etc. Vegetation is characterized within numerical weather prediction models (NWPM) and RCMs by FVC, LAI and the vegetation class [4]. Therefore, realistic characterization of these parameters should lead to a better reproduction of atmosphere-surface processes.

Different methodologies have been reported for obtaining FVC and LAI through satellite NDVI data [5–8]. According to Gutman and Ignatov [6] both parameters should not be used simultaneously in the same parameterization. Therefore, it is necessary to prescribe one of the indices and derive

the other. Gutman and Ignatov [6] and Carlson et al. [9] argued that is preferable to derive FVC and prescribe LAI, because the exponential dependence of LAI and NDVI saturates after a certain threshold, becoming LAI insensitive to changes in NDVI. Some other authors [7,10] recommend to derive LAI (fixing FVC), because they assume that spatial and seasonal variations of NDVI are related with variations of LAI and validation of FVC is problematical because of the requirement of information at the scale of individual plant elements. Anyway, Godfrey et al. [2] argue that errors introduced by the dual specification of vegetation parameters from a single NDVI observation are likely smaller than uncertainties associated to initial conditions.

The role of vegetation parameters (FVC and LAI) is relevant for weather forecasting and climate change assessments [11]. Their impact on land surface processes has been studied in the Eta operational model [12–14] and the Weather Research and Forecasting model (WRF) [15–20]. The relevance of using realistic information of the vegetation state on RCM performance has been analyzed in several works. Meng et al. [21] and Müller et al. [22] study the impact of vegetation in concrete cases of droughts in Australia and South America. Other works investigate the contribution of near real time values of vegetation fraction to simulated precipitation. For instance, vegetation–atmosphere feedback in monsoon systems [23,24], severe convection episodes [25], or improving model performance in oasis-desert systems [26]. Other interesting studies focus on vegetation effects on regional climate simulations in complex urban areas like Los Angeles [27,28].

However, the characterization of vegetation is subjected to several sources of uncertainty. On one hand, there are several NDVI database available for obtaining FVC data. They differ in aspects such as the methods for correcting errors, type of satellite, etc. The differences among these NDVI databases reach, for example, similar values to the observed trends in the phenological phases [29]. On the other hand, several methodologies can be used to obtain FVC or LAI from NDVI data [4,6,13,30]. Crawford et al. [31] points out that differences up to 25% in LAI and FVC can easily occur among the different methodologies, similar values to the FVC interannual variability.

In addition, most of the standard configurations of NWPM and RCM use climatological values of vegetation parameters. However, the vegetation has a strong inter-annual variability [18,21,31], leading this to a non-suitable characterization of surface properties. A known limitation is that surface properties can vary at several time scales depending on climate conditions and other processes such as urbanization, forest fires or changes in crops [32].

As an example of the impact of using different approaches with FVC datasets in a LSM, Miller et al. [30] describe, using the Noah LSM, noteworthy differences in surface fluxes, comparing a five-year climatology data from the Advanced Very High Resolution Radiometer (AVHRR) and NASA's Moderate Resolution Imaging Spectrometers (MODIS) data from the year 2000. With differences of 25% in FVC in monthly-averaged values for all pixels of dry land and cropland of the same two-degree box, transpiration was modified in 30 W m−2, latent heat fluxes in 10 W m−2, and sensible heat fluxes in −20 W m<sup>−</sup>2. Other works, where real-time satellite data have been used instead of climatological values, have found improvements in the forecasts of T2m in RCM simulations [13,33]. While other studies, indicates that the model sensitivity is not reliable with improved vegetation parameters [2,15,23], stressing the significance of minimizing errors in surface initial conditions (especially soil moisture) or canopy resistance parameterization.

The objective of this work is to assess the impact on T2m simulated by a RCM, of the uncertainty associated to the use of different NDVI data, different methodologies for obtaining FVC, as well as the temporal variability of surface properties in regional climate simulations. Our work will focus on IP, since it is characterized by a large variety of vegetation classes, with a strong seasonal and interannual climate variability that leads to important changes in the state of vegetation.

#### **2. Methods and Data**

#### *2.1. NDVI Data*

The NDVI is defined as:

$$NDVI = NIR - \frac{VIS}{NIR} + VIS\_{\prime} \tag{1}$$

where NIR and VIS are the amounts of near-infrared and red visible, respectively, reflected by the vegetation and captured by the satellite sensor [34]. This index was highly correlated with the photo synthetically active biomass, chlorophyll abundance and energy absorption [35] and has been widely used in studies involving land-biosphere interactions [6,13,30,31,36].

There are several global coarse-resolution satellite spectral vegetation index datasets [37]. For example, Satellite Pour l'Observation de la Terre (SPOT Vegetation) and MODIS datasets which have higher resolution and significant improvements with regard to the 1981–2004 record of the AVHRR dataset. However, the latter is an invaluable and irreplaceable archive of historical land surface information and the most used dataset for deriving NDVI due to its long record. Since 1984 several global land surface NDVI data have been derived from AVHRR, as can be the global vegetation index (GVI) dataset [38,39], the NASA Pathfinder 8-km dataset [40] and the GIMMS-NDVI[37,41]. Other datasets, such as the EFAI-NDVI [29], were derived from the NASA Pathfinder 8 km data. These datasets were corrected by taking into account artifacts from sensors, orbital drifting, atmospheric corrections and cloud screening.

Therefore, NDVI values can vary from a database to another in an remarkable way. The GIMMS-NDVI and EFAI-NDVI datasets were chosen to perform our analysis. The selection was done following criteria of NDVI data already processed, easily/free availability and temporal extension.

The EFAI-NDVI covers the period 1982–2001 and corrects the original AVHRR data to create a continuous dataset of 10-day temporal and 0.1◦ spatial resolutions with global coverage. The correction method implies a spatial interpolation of missing values and processing artifacts as well as a temporal interpolation of the NDVI series throughout a Fourier adjustment algorithm [10,29].

The GIMMS-NDVI [42] from the AVHRR dataset covers from 1982 to 2006 with a spatial resolution of 8 km. This database has been corrected for calibration, view geometry, volcanic aerosols, and other effects non related to vegetation change. In particular, NOAA-9 descending node data from September 1994 to January 1995, volcanic stratospheric aerosol correction for 1982–1984 and 1991–1994, and improved NDVI using empirical mode decomposition/reconstruction to minimize effects of the orbital drift.

#### *2.2. Deriving FVC from NDVI Data*

FVC is defined as the fraction of horizontal area associated with the photosynthetically active green vegetation that occupies a model grid cell [43]. In our experiments we derive FVC and use prescribed LAI, because of our LSM, Noah, considers this option in its configuration.

FVC calculation from NDVI data [44] can be based on linear models [6] or quadratic models [45]. These methods take as reference bare soil (*NDV I*0) and dense green vegetation(*NDV I*∞). Such values can be prescribed or estimated from the actual NDVI data. For instance, Montandon and Small [44] studied the impact of varying *NDV I*0, showing how its underestimation yields a FVC overestimation.

In this study, three linear models, WETZEL, GUTMAN and ZENG have been selected as a basis to study the uncertainty in FVC calculation and its impacts on RCMs runs. These methods present different degrees of complexity, being the WETZEL/ZENG method the most simple/complex, while GUTMAN is of intermediate complexity. This latter has been extensively used in generating data for NWPMs.

The three methodologies have been applied to the GIMMS-NDVI data. Monthly FVC values have been calculated from 1982 to 2006 with a spatial resolution of 0.1◦.

#### 2.2.1. Wetzel Method

In Chang and Wetzel [5] a two-line-segment method is presented. The slope of the linear relationship between NDVI and FVC changes when NDVI exceeds 0.547. The relation is as follows:

$$FVC = \left\{ \begin{array}{l} 1.5(NDVI - 0.1), NDVI \le 0.547\\ 3.2(NDVI) - 1.08, NDVI > 0.547, \end{array} \right. \tag{2}$$

being that the FVC is constrained to be between 0 and 1.

The FVC data obtained by this method is used in the MM5 LSM model by Crawford et al. [31] and in the ETA model by Kurkowski et al. [13].

#### 2.2.2. Gutman Method

This method uses the following simple linear relationship between NDVI and FVC:

$$FVC = \frac{NDVI\_{\text{val}} - NDVI\_0}{NDVI\_{\infty} - NDVI\_0} \,\tag{3}$$

where *NDV I*<sup>0</sup> and *NDV I*<sup>∞</sup> are the values for bare soil and dense green vegetation.

Gutman and Ignatov [6] take *NDV I*<sup>0</sup> = 0.04 and *NDV I*<sup>∞</sup> = 0.52, which correspond respectively to the minimum NDVI value of the desert cluster and the maximum NDVI value of the evergreen cluster in their study with GVI data. These values are in principle, region and season specific, since they depend on the soil and vegetation types and the vegetation chlorophyll content [46]. However, the authors take this assumption because there are many intermediate cases that make the evaluation difficult for other surface types. In this work, *NDV I*<sup>0</sup> and *NDV I*∞, have been set to 0.1 ( 2% percentile of bare soil) and 0.91 ( 98% Evergreen vegetation) using the GIMMS-NDVI database.

#### 2.2.3. Zeng Method

This method includes procedures that involve the analysis of NDVI data as a function of biome or land cover type. This approach has been used in several works [7,15,30,43]. It follows the simple relationship between FVC and NDVI described by Gutman and Ignatov [6] (Equation (3)). The values of *NDV I*∞ are calculated analyzing the frequency distribution of maximum NDVI for each land cover type.

This study uses the University of Maryland Department (UMD) global land cover classification [47,48]. This land cover database has been chosen because it was created using the same NDVI data as the GIMMS-NDVI database. Figures 1a (forest types) and 2a (shrubland, cropland and grassland) show the spatial distribution as well as the relative frequency distributions of NDVI.

According to Zeng et al. [7] *NDV I*∞ is low sensitive to the exact percentile used for a given vegetation type. In this work the *NDV I*∞ is chosen getting the 98th percentile as adopted in other studies ([10,43]). For *NDV I*0, the 5th percentile of the no-vegetation category is taken for all vegetation types. North Africa has been included to have a more suitable value for bare soil. Regarding *NDV Ival*, Zeng et al. [7] estimated FVC as independent of season, then *NDV Ival* (Equation (3)) is the annual maximum in a given pixel in order to minimize the effects of cloud contamination. However, in our case, *NDV Ival* corresponded with each of the 15-day values of the GIMMS-NDVI database in a given pixel, since we precisely wanted that FVC varies with time.

**Figure 1.** (**a**) Area covered by land type. Relative frequency distributions of NDVI in the IP by land types according to UMD global landcover. (**b**) FVC time series in forest land types calculated with the GUTMAN (green solid line), WETZEL (blue solid line) and ZENG (red solid line) methods (spatial average by land type). Differences between FVC methods: WETZEL-ZENG (red dashed line), WETZEL-GUTMAN (green dashed line), ZENG-GUTMAN (blue dashed line). (**c**) Same as b but monthly averaged values by land type for the period 1982–2006.

**Figure 2.** As Figure 1 for closed and open shrubland, grassland and croplands.

As a help for understanding differences between the methods, Figure 3 shows the relationships between FVC and NDVI for the three methods. The ZENG method (red area) provides a wider range of values of FVC depending of land type frequency distribution of NDVI. The GUTMAN method (green line) agrees with the lower correspondence between NDVI and FVC of the ZENG method, explaining the lower values of FVC. On the other hand, the WETZEL method produce higher FVC values in areas where NDVI is larger than the rupture point (0.547), especially in the north of IP.

**Figure 3.** Relationship between fractional vegetation cover (FVC) and nearest difference vegetation index (NDVI) for the ZENG (red area), GUTMAN, WETZEL, and GUTMAN quadratic methods.

#### *2.3. Regional Climate Model Experiments*

In order to gain some insight about the sensitivity of a RCM to changes in FVC a set of simulations have been performed using a climate version of the MM5 mesoscale model [49,50]. The spatial configuration consists of two-way nested domains with 30 and 10 km horizontal resolution respectively (Figure 4) and 24 sigma levels in the vertical up to 100 mb. ERA-Interim reanalysis [51] were used to provide initial and boundary conditions, which were updated every six hours. The physics configuration consisted of Grell cumulus parameterization [52], simple ice for microphysics [53], rapid radiative transfer mode (RRTM) radiation scheme [54] and the medium-range forecast (MRF) planet boundary layer scheme [55]. The Noah LSM [56] used in this simulations was based on the coupling of the diurnally dependent Penman potential evaporation approach of Mahrt and Ek [57], the multilayer soil model of Mahrt and Pan [58], and the primitive canopy model of Pan and Mahrt [59]. It was extended by Chen and Coauthors [60] to include a complex canopy resistance approach [61,62] and a surface runoff scheme. The prognostic variables in the Noah LSM are soil moisture and temperature as well as canopy moisture and water-equivalent snow depth. Four soil layers were used with thicknesses of 10, 30, 60 and 100 cm, and an additional canopy layer for the soil model. The total soil depth was 2 m, with the root zone in the upper 1 m of soil. The lower 1-m soil layer acted as a reservoir with gravity drainage at the bottom. Ground heat flux was controlled by the usual diffusion equation for soil temperature, with heat capacity and thermal conductivity formulated as functions of the soil water content. The diffusive form of Richard's equation was used as the prognostic equation for the volumetric soil moisture content, where the hydraulic conductivity and the soil water diffusivity are also functions of the soil water content. In this LSM, vegetation type and soil texture were primary variables upon which other secondary parameters (such as minimal canopy resistance and soil hydraulic and thermal properties) were determined. Landuse-vegetation category is specified from 25-category 1 km data from USGS version 2 land cover data. The dominant vegetation type in each grid box is selected to represent the "grid level" vegetation characteristic. In Noah LSM, the FVC acts as a fundamental weighting coefficient in partitioning the total evaporation into direct evaporation from the top shallow soil layer, evaporation of precipitation intercepted by the canopy, and transpiration via canopy and roots.

The runs have been performed running the model for one year with four additional months of spin-up period. The outputs during this spin-up period, integrated only to guarantee that soil variables reach dynamic equilibrium, are ignored. The RCM used here, employing similar configurations, provided satisfactory results in climatic simulations [50,63–65].

**Figure 4.** Regional climate model (RCM) spatial domains. Mother domain, D1 (30 km), and inner domain, D2 (10 km). Topography of the spatial domains.

Most regional models such as WRF or MM5 (Noah LSM) use a default FVC monthly climatological data calculated in a given period. A commonly used database is described in Gutman and Ignatov [6] which consist of a 5-year FVC climatology derived from AVHRR, for the period April-December of 1985, from 1986 to 1990 and the period January–March of 1991 with a spatial resolution of 0.15◦. In this work, the database generated had a resolution of 0.1◦ × 0.1◦.

Two set of experiments have been carried out. The first set investigated the effect of using different methodologies in constructing the FVC database used in the simulations. For this task, firstly a five-year FVC climatology has been constructed for the same period of Gutman and Ignatov [6] (April 1985–March 1991) with the ZENG, GUTMAN and WETZEL methods from the GIMMS-NDVI data. Then three runs for the year 1995 were performed using such FVC climatologies. The second set of simulations explored the role of introducing synchronous FVC data for a given year respect to the use of climatological values of this variable. For this case, two pairs of runs were carried out, choosing the ZENG method for the years 1995 (a dry year) and 1996 (a wet year). Each pair consists of a simulation using the period of the five-year FVC climatology (CLIM) described before and the other one using the FVC values for such year (YEAR). The ZENG method has been chosen, among the three options, because it presents an intermediate behaviour between WETZEL and GUTMAN (see Figure 5) as well as the fact that it has been evaluated in previous works [7].

**Figure 5.** (**a**) FVC spatial averaged time series (solid lines) and differences between them (dashed lines) for the whole IP calculated by the WETZEL (blue line), GUTMAN (green line) and ZENG (red line) methods. (**b**) FVC temporal averaged over the whole period (1982–2006) for the IP for the ZENG, WETZEL and GUTMAN methods.

#### **3. Results**

#### *3.1. NDVI Database Comparison*

Figure 6 shows the NDVI for the two datasets in a dry year (1995) and a wet year (1996) for January and July over the IP. It is remarkable that they differed in magnitude but that spatial variability was quite similar (spatial correlation over 0.97). The differences were higher when comparing years with different precipitation regime, where NDVI values differed up to 30% in the summer (not shown).

The 12 month run-mean series of the spatially averaged NDVI over the IP for the period 1982–1998 is shown in Figure 7a. The series shows a quite different interannual variability for the whole period; the EFAI-NDVI had a greater variability and lower mean values than the GIMMS-NDVI. We attributed the disagreement (almost a periodical signal not attributable to natural variability) to the solar zenith angle correction implemented in the GIMSS-NDVI that avoids artificial trends derived from orbital

drifting. At this point we concluded that the EFAI-NDVI should not be used for characterizing the vegetation properties in RCMs. Therefore, we focused our study on the GIMMS-NDVI data. In addition, GIMMS-NDVI had a longer record, because of the compatibility with AVHRR data with MODIS and SPOT vegetation satellite data, that have continued until nowadays.

**Figure 6.** EFAI-NDVI and GIMMS-NDVI for January and July for a wet year (1996) and a dry year (1995) in the Iberian Peninsula (IP).

**Figure 7.** (**a**) NDVI 12 month run-mean of the spatially averaged series for the IP derived from the EFAI-NDVI (red line) and GIMMS-NDVI (green line); (**b**) GIMMS-NDVI monthly values for a wet year (1996, green line), a dry year (1995, orange line) and climatology (blue line).

Usually FVC is implemented using climatological values in NWPMs and RCMs. We compared the spatially averaged NDVI over the IP, with monthly climatological values calculated on a seven year period (1985–1991) with the monthly evolution on a specific dry (1995) and wet year (1996) (Figure 7b). The biggest NDVI differences between the dry and wet year were observed in spring, when the maximum of vegetation productivity occurred, prevailing these NDVI differences in a lesser way the rest of the year. The spatial average NDVI difference between wet and dry years was about 5% reaching values up to 15% in some areas. This elucidates that taking into account the interannual variability could be important when implementing these data in RCMs.

#### *3.2. Analysis of FVC Retrieval Methods*

FVC data were obtained for the 25 year of available NDVI data using the three methods. Figure 5a shows the temporal evolution of the FVC data spatially averaged over the IP and Figure 5b the temporal means of FVC values over the domain. Since the three methods used the same NDVI, there was a very good agreement in the temporal variation of FVC, however some important differences appear. The most remarkable difference comparing the magnitude of FVC was that WETZEL method presented FVC values 10% greater than the others. The ZENG and GUTMAN methods only differed around a 3% in magnitude. This large difference was due to the fact that when applying WETZEL method the frequency distribution of NDVI was not taken into account. Regarding the spatial variability, the ZENG and GUTMAN show very similar patterns although the ZENG method achieves a larger range of FVC values, reaching values near 10% and over 80% more frequently.

The differences between the methods depended on the land type by construction. Figures 1b and 2b depict the time series of the FVC spatially averaged by land types calculated with the GUTMAN (green solid line), WETZEL (blue solid line) and ZENG (red solid line) methods as well as the differences between them (dashed lines). The GUTMAN and ZENG methods showed very similar FVC values in forest land types (Figure 1), while the WETZEL method showed significant differences of around 20% respect to the other methods. Land types with low values of FVC, such as shrubland, cropland and grassland (Figure 2) present smaller differences. In the case of closed and open shrubland, the WETZEL method presents the closest values to the other methods. This can be explained because NDVI values below the rupture point of the WETZEL method were more frequent for this land types (Figure 2a).

Therefore, in general, places with high FVC values exhibited a good agreement between the methods that take into account the NDVI frequency distribution (GUTMAN and ZENG), showing larger differences with the WETZEL method. While in places with medium and low values of FVC, the spread among the methods was lower.

Another interesting feature was to evaluate is the differences in phenology. Figures 1 and 2 show the relative frequency distribution if NDVI (a), temporal evolution (b) and FVC annual cycle (c) for each biome and the differences between them. In the biomes where the maximum of FVC occurred in summer, forest vegetation with low hydric stress, the GUTMAN and ZENG methods had a quite similar behavior (maximum value of 60–80% in summer) while the WETZEL method gave greater values. In the rest of the biomes, the maximum of FVC occurred in spring (irrigated lands or places with summer hydric stress) and the differences between the methods were smaller than for forest land types, although there were differences that could be remarkable in cropland, grasslands and woodland areas.

#### *3.3. Sensitivity to the FVC Estimation Method*

Here we assessed the sensitivity of climate simulations to the use of different FVC data presented above. For the sake of clarity, we only present the comparisons between the ZENG and WETZEL experiments, because they show the largest FVC differences. Figure 8a depicts the FVC monthly means of ZENG for some months representative of each season (January, April, July and October) and Figure 8b the FVC differences between the ZENG and WETZEL methods. There is a clear negative difference in the FVC estimated (ZENG-WETZEL) with higher FVC values for the WETZEL data, especially in areas with the highest FVC. The biggest difference was in April, when the vegetation productivity presented the maximum rate of generation of biomass.

The effect of such differences between the ZENG and WETZEL data on the regional climate simulations for monthly mean T2m is depicted in Figure 8c,d. In general, lower FVC values led to higher temperatures, but this relationship depended on the time of the year (Figure 8d). This result was directly related to the sensible and latent heat partitioning (not shown). An increase/decrease of vegetation led to a larger/lower evapotranspiration and therefore, a higher/smaller latent heat production. This led to a reduced/intensified sensible heat manifested in a decrease/increase in T2m. Besides, there was an additional effect due to the modification of soil thermal conductivity by the presence of a vegetation canopy. Heat flux from soil was reduced when FVC increases because of heat conductivity was diminished [12,66]. This effect was of importance mostly during night when these fluxes had a dominant role and can cause higher night temperatures with reduced FVC.

It is worth noting that T2m differences were higher in April and July than in January and October with similar FVC differences (Figure 8). In January differences of 30% in FVC led to changes smaller than 0.5 ◦C, while in July or April can reach 1 ◦C in some places. This was mainly due to the greater availability of energy at these months. In addition, the greater dispersion in the quasi-linear relationship between changes in FVC and T2m (Figure 8d) for higher Δ*FVC* values may be relevant. Therefore, it was clear that changing the FVC database modified the climatology reproduced by a RCM, both the spatial patterns and the amplitude of the annual cycle.

**Figure 8.** (**a**) Monthly means of FVC for the ZENG method. (**b**) Differences of monthly means of FVC between the ZENG and WETZEL methods. (**c**) Differences in monthly means of T2m between the ZENG and WETZEL regional climate simulations. (**d**) x–y scatter plot graph between differences of FVC (x-axis) and differences in T2m between ZENG and WETZEL methods(y-axis).

#### *3.4. Sensitivity to FVC Interannual Variations*

As mentioned above, in RCM experiments, FVC values are usually prescribed using climatologies, so they do not vary from year to year. But vegetation depended on the climate conditions as well as other factors, and it had a non negligible interannual variability. In this section we analyze the effect of varying the FVC according to the observations (real FVC) versus fixed monthly climatological/prescribed values.

Figure 9 shows the differences between the FVC 5-year climatology simulation (CLIM) and the reconstructed (YEAR) for January, April, July and October for two years, the dry 1995 and wet 1996. These years were selected because of their quite different climate conditions. Dry conditions prevailed during 1995, while 1996 was characterized for abundant precipitation that favored higher biomass production.

**Figure 9.** Differences between five-year FVC climatology (CLIM) simulation (climatology-based FVC) and FVC values for such year (YEAR) simulation (inter-annual varying FVC) for FVC and T2m in a dry year (1995) and a wet year (1996).

During the dry year, the prescribed FVC was much higher that the estimated for this year, finding large areas with Δ*FVC* over 10%, especially in April and July that locally reached values around 30%. The T2m temperature patterns resembled the spatial structure of Δ*FVC* with a clear negative bias. The largest T2m differences appeared again in July and April, with values larger than 0.4 ◦C in large areas that locally were over 0.8 ◦C.

During the wet year the largest Δ*FVC* appeared in April in the north–west part of the IP, and October over the South and Western Iberia. In this case differences were negative, i.e., the prescribed FVC underestimates the YEAR FVC. Interestingly, the absolute T2m differences were smaller, with slightly lower absolute Δ*FVC*. This is due to the fact that in July (highest sensitivity) Δ*FVC* is quite small, and that in April advection phenomena prevailed in the area with the largest Δ*FVC*.

Adding both effects at the different times suggest that interannual variability can change more that 1 ◦C in some areas for a given month.

#### **4. Discussion and Conclusions**

Three database of monthly FVC from 1982 to 2006 has been created for a domain covering the whole IP with a spatial resolution of 0.1◦ with the aim of being implemented in RCMs to improve climate simulations by providing more realistic soil physical conditions. Three different methodologies, WETZEL, GUTMAN and ZENG, were applied to the same NDVI database. The GIMMS-NDVI data was selected because it has better interannual behavior, longer record and is compatible with MODIS and SPOT vegetation data, which facilitates its temporal extension.

The comparison between the FVC databases reveals important differences between them that depend on the FVC value and the biome, being especially relevant for forest land types. Methods that use the frequency distribution of NVDI (ZENG and GUTMAN) are more similar although some differences can be found between them depending of the land type. In addition the FVC series reveal a important interannual variability, consequently prescribed FVC values can present important differences regarding the "real" FVC. These facts can be important when FVC are used in RCMs coupled to LSMs. The magnitude of the differences found causes a noteworthy impact on surface fluxes and hence modifying the regional climate.

The RCM experiments performed exhibit a not negligible effect of FVC uncertainty on the monthly climatological values. The results showed that differences of 30% of FVC, that appear in the two sensitivity experiments, can produce bias of 1◦ in T2m monthly values. Therefore, depending on the spatial structure of the FVC differences, the climatological patters are modified. In addition, the magnitude of the model response depends on the time of the year. This implies that the annual cycle reproduced by the model is also sensible to the FVC data used. Finally, the role of the interannual variability of the FVC series can also change the interannual variability of climatological series (T2m in our study). In the experiment showed here, the differences for some times of the year, especially spring and summer could reach 1 ◦C.

It is known that changes in vegetation also modifies the albedo. This was not taken into account in our experiments. Therefore, it can be expected an impact on the regional climate simulations. In fact, this impacts can be opposite to our findings since a decrease of the vegetation leads to an increase of albedo and then, less radiation is trapped by the surface and temperature could decrease. However, Crawford et al. [31] show that the impact of changing albedo on surface heat fluxes is much smaller, an order of magnitude, than changing FVC.

In this paper we just analyze the sensitivity of RCM simulations to changes in FVC. A question remaining is if the RCM simulations with these new FVC datasets can better capture the climate over the IP. The sensitivity of the model to the FVC changes is much smaller than the deviations of the model respect to the observations (not shown), therefore it is difficult to find any improvement using only one or two years of data. This is not a surprising result. For example, Gómez-Navarro et al. [67] shows that the skill score of different RCMs depends on the data-base chosen for evaluating the model. Other examples can be found in Fernández et al. [68] and Jerez et al. [64] where the authors did not find any combination of physical parameterizations that always reproduce better the observed climate. However, changing FVC with time instead of using climatological data should improve the simulation of inter-annual regional climate variability. For instance, the mean temperature differences between the dry and the wet year are better represented by the YEAR simulations (not shown).

In this work, just linear models have been tested for obtaining FVC from NDVI, because they have been more frequently used (for instance, in most of works cited along the work). Some examples are the NOAH land–surface model and the NAM Eta model. However, some authors use quadratics models. Figure 3 shows the relationship between FVC and NDVI for the linear models analyzed and a quadratic model (GUTMAN Quadratic). The quadratic methodology drives to lower values of FVC. Anyway, the FVC differences obtained with linear methods covers the range of the differences in FVC that we found.

**Author Contributions:** Conceptualization, J.M.J.-G. and J.P.M. Methodology, J.M.J.-G. and J.P.M. Software, J.M.J.-G. and J.P.M. and S.J. Investigation, J.M.J.-G. Formal Analysis, J.M.J.-G., J.P.M. and S.J. Data curation, J.M.J.-G., J.P.M. and S.J. Writing—original draft preparation, J.M.J.-G. and J.P.M. Writing—review and editing, J.M.J.-G., J.P.M., S.J. and F.V. Visualization, J.M.J.-G. Supervision, J.P.M. and F.V.

**Funding:** The authors acknowledge projects REPAIR-CGL2014-59677-R and ACEX-CGL2017-87921-R of the Spanish Ministry of Economy and Competitiveness PCIN-2014-013-C07-04, PCIN2016-080 (UE ERA-NET Plus NEWA Project), CGL2016-78702, and the Instituto de Matemática Interdisciplinar (IMI) of the Universidad Complutense that partially support this work. S. Jerez has received funding from the Plan Propio de Investigación of the University of Murcia (grant No. UMU-2017-10604) and the Fundación Séneca—Regional Agency for Science and Technology of Murcia (CLIMAX project 20642/JLI/18).

**Acknowledgments:** We acknowledge all the institutions and communities that provided free software, R community, CDO (Climate Data Operators), GMT (Generic Mapping Tools), MM5, Gnuplot, gfortran as well as the institutions supplying data (ECMWF, NASA).

**Conflicts of Interest:** The authors declare no conflicts of interest.

#### **References**


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