In this section, we first evaluate the performances of CMIP6 models in reproducing the meteorological elements across global drylands, given the aforementioned meteorological anomalies as the main drought drivers. Figure 1 shows the spatial patterns of the climatology of the annual precipitation, temperature, PET, and P–PET in drylands from the observations, the simulated CMIP6 multi-model ensemble mean (MME), and their respective differences. Observations show the climate mean annual precipitation, temperature, and PET averaged over global dryland regions to be 328 mm, 17.8°C, and 1580 mm, respectively (Figs. 1a, d, g). Because the atmospheric evaporative demand is higher than the water supply, global drylands are completely under water deficit conditions, with P–PET reaching –1252 mm on average (Fig. 1j). The climatological values of the four meteorological elements are unevenly distributed across global drylands. The annual precipitation amount agrees with the spatial pattern of aridity, with the least rainfall (<25 mm) observed in the hyperarid North Africa-Middle East and western Australia areas, while relatively greater rainfall (>500 mm) occurred in the semihumid and semiarid Sahel, India Peninsula, and northern Australian areas (Fig. 1a). The climate mean annual temperature, PET, and P–PET show similar patterns to each other, particularly with the highest temperature (>28°C), atmospheric evaporative demand (>2400 mm) and the most severe water deficit condition (<–2400 mm) in the hyperarid North Africa-Middle East and western Australian regions (Figs. 1d, g, j).
Spatial patterns of observed and simulated climatology in four meteorological elements across global drylands during 1980–2014. (a–c) Annual precipitation (units: mm yr–1), (d–f) annual mean temperature (units: °C), (g–i) annual potential evapotranspiration (PET, units: mm yr–1), (j–l) annual water balance (P–PET, units: mm yr–1). The three columns depict observation, CMIP6 multi-model ensemble mean (MME), and biases. Slant hatchings in the right column denote that 21/27 models are of the same sign, and area-averaged biases are presented as MME and inter-model range between minimum and maximum, respectively.
The spatial distributions for the climate mean states of the four meteorological elements from CMIP6 MME agree well with the observations, with the PCC of simulated annual precipitation, temperature, PET, and P–PET reaching 0.8, 1.0, 0.8, and 0.8, respectively (Figs. 1b, e, h, k). The annual precipitation is overestimated in most drylands except the northern African, Middle East, and India peninsula regions, on average by 107.6 mm (~33% of observed climate mean), with the RMSE reaching 250.5 mm (Figs. 1b, c). In contrast, the annual temperature and PET in drylands are systematically underestimated by CMIP6 models (Figs. 1e, f, h, i), especially for PET with an area average that is 500 mm (~32%) lower than observations (Figs. 1h, i). Due to the combination of overestimated precipitation and underestimated PET, CMIP6 MME shows a systematic overestimation of P–PET across all dryland areas, as evidenced by an area mean (–607 mm) that is about 48% less than the observation (–1252 mm) (Figs. 1k, l). The largest bias also occurs in the hyperarid North Africa-Middle East and western Australia regions, exceeding 1500 mm, thereby indicating much weaker water deficit conditions in drylands simulated by CMIP6 models relative to observations. According to Eq. (1), PET is determined by Tas, ea, Rn, and u2. Considering the observational data availability to calculate PET in CRU TS v.4.03, we checked the relative contributions of the four factors to PET bias and found that the systematic underestimation in PET mainly comes from biases in radiation and wind speed, especially over the hyperarid North Africa and Middle East drylands (figures not shown).
We further examine the performances of CMIP6 models in the simulation of interannual variability of the four meteorological elements depicted in Fig. 2. The observed are-averaged, interannual variability of annual precipitation, temperature, PET, and P–PET in drylands is 69.2 mm yr–1, 0.5 °C yr–1, 39.9 mm yr–1 and 95.9 mm yr–1, respectively, with obvious regional differences (Figs. 2a, d, g, j). The interannual variability in both precipitation (Fig. 2a) and P–PET (Fig. 2j) shows a high consistency with the annual precipitation climatology (Fig. 1a), with the lowest SD (<20 mm yr–1) observed in the hyperarid regions and the largest SD (>200 mm yr–1) in the semiarid and semihumid areas. As for annual mean temperature (Fig. 2d), the observed interannual variability in the northern mid-latitude drylands (>1.0°C yr–1) is notably higher than the tropical and Southern Hemisphere drylands (0.2°C yr–1–0.4°C yr–1). The observed interannual variability of PET (Fig. 2g) is generally below 60 mm yr–1 over most drylands, except for the North American (>100 mm yr–1), central Asian, and western Australian areas (~70 mm yr–1).
Same as in Fig. 1, but for the interannual variability of four meteorological elements during 1980–2014. (a–c) Annual precipitation (units: mm yr–1), (d–f) annual mean temperature (units: °C yr–1), (g–i) annual potential evapotranspiration (PET, units: mm yr–1), (j–l) annual water balance (P–PET, units: mm yr–1).
CMIP6 models well reproduce the spatial patterns for the interannual variability of precipitation, temperature, and P–PET, but fail to capture that for PET, with their PCC of simulation reaching 0.9, 0.8, 0.8, and only 0.1, respectively (Figs. 2b, e, h, k). Overall, CMIP6 models overestimate the interannual variability of precipitation, temperature, and P–PET in most drylands, on average by 25.6 mm yr–1 (~37% of observed SD) and 0.2°C yr–1 (~38%), and 18.7 mm yr–1 (~19%), respectively, while underestimate that of PET by –6.9 mm yr–1 (~17%). For precipitation (Fig. 2c) and temperature (Fig. 2f), the overestimations of their interannual variability are widespread across most drylands. The largest biases are seen in the Southern Hemisphere for precipitation (>60 mm yr–1) and in many dryland regions for temperature (>0.4°C yr–1). Biases for the interannual variability in PET (Fig. 2i) and P–PET (Fig. 2l) present heterogeneous patterns. For PET, the largest overestimation (>40 mm yr–1) is seen in the western North Africa and India Peninsula regions, while the largest underestimation (<–50 mm yr–1) is located over the North American drylands. For P–PET, positive biases within 20–80 mm yr–1 are widely seen over most drylands except for slight negative biases (~20 mm yr–1) in the northern mid-latitudes. Notably, the CMIP6 models well simulate the spatial patterns of the interannual variability in precipitation and P–PET but perform poorly for PET, suggesting that the interannual variability of P–PET is dominated by precipitation.
We extend our analysis to the long-term trends over the time period 1980–2014 in Fig. 3. The observation presents an overall increasing trend in the annual precipitation, temperature, and PET across global drylands, with average rates of 11.3 mm (35 yr)–1, 0.85°C (35 yr)–1 and 48.6 mm (35 yr)–1, respectively (Figs. 3a, d, g). The trend in annual precipitation shows heterogeneous patterns, with a trend of wetting [>100 mm (35 yr)–1] in the Sahel, South African, India Peninsula, and northern Australian drylands with drying [<–70 mm (35 yr)–1] in many other dryland areas. The warming rate is particularly faster in the Afro-Asian drylands [>1.5°C (35 yr)–1] compared to the other drylands. Because the rising atmospheric evaporative demand is partly offset by increased precipitation, the annual P–PET has generally declined at –36 mm (35 yr)–1 over global drylands (Fig. 3j), showing a similar pattern with annual precipitation. The decreasing trend in the annual P–PET suggests deteriorating water deficit conditions over most drylands except for those regions with significant wetting trends.
Same as in Fig. 1, but for the long-term trend in four meteorological elements during 1980–2014. (a–c) Annual precipitation [units: mm (35 yr)–1], (d–f) annual mean temperature [units: °C (35 yr)–1], (g–i) annual potential evapotranspiration [PET, units: mm (35 yr)–1], (j–l) annual water balance [P–PET, units: mm (35 yr)–1]. Dots in the left column denote those regions passing the 0.05 significance level, and slant hatchings in the rightmost two columns indicate that 21/27 models are of the same sign.
Although CMIP6 simulations agree with the observation on the sign of the long-term trend in the four meteorological elements over global drylands, the regional patterns are poorly reproduced, as evidenced by the low PCC of simulations within 0.2–0.5 and large RMSEs (Figs. 3b, e, h, k). The biases for the long-term trend in all meteorological elements show obvious regional dependence. The simulated annual precipitation presents an increasing trend over the observed drying regions, including most Northern Hemispheric and South American drylands; thus, the long-term trend is overestimated by about 80 mm (35 yr)–1, at most (Figs. 3b, c). In contrast, the increasing trend is greatly underestimated by about 80 mm (35 yr)–1 over those observed drylands which are wetting. The simulated annual mean temperature shows an overestimated warming trend in most drylands, on average by 0.42°C (35 yr)–1 (49% of the observation) (Figs. 3e, f), while an underestimated rising trend in PET, on average by 15.5 mm (35 yr)–1 (32% of the observation) (Fig. 3h, i). As a result, CMIP6 models systematically overestimate the trend in annual P–PET by 16.3 mm (35 yr)–1 (45% of the observation) across global drylands (Figs. 3k, l), showing a similar spatial pattern to that of annual precipitation.
As the CMIP6 models still show spread in both the mean state and variability simulations, we further assess the performances of individual models using a portrait diagram (Fig. 4). Among the three metrics, the simulated climatology generally obtains the highest scores (0.6–0.98), followed by the interannual variability (0.4–0.8); the lowest scores were obtained for the long-term trend (0.4–0.6). In terms of the four meteorological elements, the simulation skill in temperature is the highest, followed by precipitation and P–PET; the lowest skill was for PET. For the temperature climatology, in particular, all CMIP6 models score above 0.95, close to the perfect score of 1.0. Most models perform poorly in simulating the interannual variability of PET and the long-term trend in the four meteorological elements, with scores generally less than 0.6. Notably, the performance of MME is substantially higher than any individual model except for simulating the long-term trend for precipitation and PET.
Portrait diagrams of skill scores for each CMIP6 model in simulating the meteorological elements across global drylands during 1980–2014. The horizontal axis denotes 27 CMIP6 models and the multi-model ensemble mean (MME), and the vertical axis indicates the assessed metrics, including the climatology (CLM), interannual variability (STD), and long-term trend (Trend) for precipitation (Pre), temperature (Tas), potential evapotranspiration (PET) and water balance (P–PET). Warmer colors represent higher skills, with a perfect score of 1.0.
According to the calculation method of SPEI (Vicente-Serrano et al., 2010), the probability density function (PDF) of P–PET can substantially affect the drought index. Thus, we investigate the performance of CMIP6 models in their ability to accurately capture the PDFs related to the water balance across global drylands. Figure 5 shows the PDFs of the 6-month accumulated P–PET for global and seven sub-drylands during 1980–2014. For global drylands, the simulated PDF (blue lines in Fig. 5) of P–PET presents an obvious rightward shift compared to the observation (red lines in Fig. 5). This suggests that much weaker water deficit conditions are simulated by CMIP6 models. The PDFs for the seven sub-drylands also show similar discrepancies with different means and standard deviations; even though the same SPEI thresholds were applied for measuring droughts, the associated water deficit conditions in CMIP6 models are quite different from the observations, which may further result in different responses of ecosystems to climate change.
Probability density functions (PDFs) of 6-month accumulated precipitation minus potential evapotranspiration (P–PET) for global and sub-drylands during 1980–2014. Red and blue lines denote PDFs derived from observation and CMIP6 multi-model simulations, and the numbers to the right are the mean and the standard deviation for PDFs. Boxes in the map depict the regions of seven sub-drylands, including the northern American (NAm), northern African (NAf), central Asian (CAs), East Asian (EAs), southern American (SAm), southern African (SAf), and Australian (Aus) drylands.
In this section, we examine the performance of CMIP6 models in depicting the climatological characteristics of drought events and the associated hydrothermal conditions across global drylands. The spatial patterns of the four drought categories are generally similar (figures not shown); thus, only severe droughts are shown herein to illustrate the model performances.
Figure 6 presents the climate mean spatial distributions for the severe drought intensity, occurrence, and duration in observations and the CMIP6 MME as well as their differences. In observations, the severe drought intensity in drylands is around –1.8 on average, with an evenly-distributed pattern (Fig. 6a). The observed occurrence (Fig. 6d) and duration (Fig. 6g) of severe drought are highly consistent, with an area average of 3.2 month yr–1 and 4.3 months, respectively, across global drylands. The most frequent and the longest-lasting severe droughts occur in the hyperarid areas of North Africa-Middle East and East Asia, with the occurrence and duration exceeding 4.0 month yr–1 and 5.5 months, respectively. The mean characteristics of droughts across drylands are not well captured by CMIP6 models, with the PCC of simulated drought intensity, occurrence, and duration only reaching 0.1, 0.3, and 0.1, respectively (Figs. 6b, e, h). The severe drought intensity is overestimated by –0.06 (–0.11 to –0.03) on average (Fig. 6c), while both the occurrence (Fig. 6f) and duration (Fig. 6i) are generally underestimated by CMIP6 models, with the largest biases exceeding –2 month yr–1 and –3 months, respectively, centered in those drylands where the most frequent and the longest lasting droughts are observed (Figs. 6d, g).
Spatial patterns of the observed and simulated climatology in severe drought characteristics across global drylands during 1980–2014; (a–c) intensity, (d–f) occurrence (units: month yr–1), (g–i) duration (units: month). Slant hatchings in the right column denote that 21/27 models have the same sign. Area-averaged biases are presented as MME and inter-model range between minimum and maximum, respectively.
The spatial patterns for the climatology of meteorological anomalies during severe droughts from observation and CMIP6 MME and model biases are further depicted in Fig. 7. The observation shows less precipitation, warmer temperatures, higher evaporative demand and increased water deficit during severe droughts, with an area average of –11.1 mm month–1, 0.6°C, 6.0 mm month–1, and –17.1 mm month–1, respectively, across global drylands (Figs. 7a, d, g, j). The anomalies of precipitation and P–PET during severe droughts show a similar unevenly-distributed pattern, ranging from less than –5 and –10 mm month–1 over the hyperarid hinterland, to greater than –30 and –40 mm month–1 over semi-humid regions, respectively (Figs. 7a, j). The temperature and PET anomalies in the Northern Hemispheric drylands are generally higher than those in the Southern Hemisphere, with centers exceeding 1.6°C and 15 mm month–1 in the North American, central, and East Asian drylands, respectively (Figs. 7d, g).
Same as in Fig. 6, but for the mean meteorological anomalies composited from all severe droughts across global drylands during 1980–2014. The anomaly of each element is calculated as the previous 6-month averaged anomaly when severe drought occurs, relative to their climatology during 1980–2014 (the same below). (a–c) Precipitation (unit: mm month–1), (d–f) temperature (units: °C), (g–i) potential evapotranspiration (PET, units: mm month–1), and (j–l) water balance (P–PET, unit: mm month–1).
CMIP6 models well reproduce the overall meteorological anomalies of severe droughts. The simulations agree well with observations regarding the spatial patterns of precipitation and P–PET anomalies (PCC=0.8) (Figs. 7b, k) but show large inconsistencies in terms of temperature and PET anomalies, with PCCs only reaching 0.3 and even –0.1, respectively (Figs. 7e, h). The precipitation deficit of severe drought is systematically overestimated by –3.1 mm month–1 (~28% of observed climatology) in most drylands except the Sahel, Mediterranean and India Peninsula regions (Fig. 7c). The biases in the temperature anomalies of severe drought show great regional differences, with positive biases (>1.0°C) present in most drylands, while negative biases (<–1.0 °C) are centered in the North Africa-Middle East drylands (Fig. 7f). The simulated PET anomaly of severe drought shows slight biases (within ±4 mm month–1) in most drylands, with the largest negative bias centers (<–10 mm month–1) over the North American dryland (Fig. 7i). Due to the combination of biases in precipitation and PET anomaly during severe drought, the simulated P–PET anomaly shows an overall negative bias of –4.5 mm month–1 (~24% of the observed climatology) in most drylands (Fig. 7i). This indicates that the magnitude in water deficit of droughts in drylands as simulated by CMIP6 models is more severe than observations.
A comparison of individual CMIP6 models in reproducing the severe drought characteristics and the corresponding meteorological anomalies is given in Fig. 8. CMIP6 models perform poorly for the climate mean drought characteristics, with most skill scores between 0.4–0.5. Of the four meteorological anomalies during severe droughts, precipitation and P–PET obtain relatively higher scores of 0.6–0.8, followed by temperature with 0.4–0.6; the lowest skill scores belong to PET, for which the scores are less than 0.5. In addition, the skill scores for the other three drought categories are generally consistent with severe droughts (figures not shown).
Same as in Fig. 4, but for three drought metrics and four corresponding meteorological anomalies during severe droughts (SPEI≤–1.5) across global drylands during 1980–2014. The vertical axis denotes the assessed variables, including severe drought intensity, occurrence, and duration, and corresponding anomalies of precipitation (Pre), temperature (Tas), potential evapotranspiration (PET), and water balance (P–PET) during severe droughts.
To investigate the ability of CMIP6 models to reproduce the responses of droughts to global warming, the time series for area fraction and occurrence for the four drought categories across global drylands during 1980–2014 are illustrated in Figs. 9a–h. The observations show that the fractional area and occurrence for all drought categories have continuously increased since the 1980s, especially and abruptly after the late 1990s (red lines in Fig. 9a–h). CMIP6 MME (blue lines in Fig. 9a–h) can well simulate the long-term increasing trends for the drought fraction area and occurrence, but with an obvious underestimation after the late 1990s. In addition, the observed time series falls within the inter-model spreads (grey shadings in Figs. 9a–h). Hence the long-term change of dryland droughts is an externally forced signal.
Changes in metrics for different drought categories across global drylands during 1980–2014. The two columns illustrate the time series (a–h) and long-term trend (i–j) for the drought area (a, c, e, g, i, units: %) and occurrence (b, d, f, h, j, units: month yr–1), respectively. (a–h) Red and blue lines denote the observations and CMIP6 multi-model ensemble mean (MME), respectively, and grey shadings denote inter-model spreads between minimum and maximum. (i–j) Box-whisker plots illustrate the minimum, first quartile, median, third quartile, and maximum of multi-model long-term tendencies. The asterisks and dots denote the observations and CMIP6 MME, respectively.
We further quantitatively compare the observed and simulated long-term tendencies for the drought-affected area and occurrence, as depicted in Figs. 9i, j. The observed fraction area (occurrence) for mild, moderate, severe, and extreme droughts in drylands have increased by ~30 (~2.2), ~20 (~2.1), ~5 (~1.5), and ~1 (0.2) % (month yr–1) from 1980 to 2014, respectively (red asterisks in Figs. 9i, j). CMIP6 models well reproduce their increasing tendencies (box-whisker plots in Figs. 9i, j). Consistent with the results in Figs. 9a–h, the simulated tendencies of CMIP6 MME for mild and moderate droughts are weaker than observations, with a rate of 10 % (35 yr)–1–12 % (35 yr)–1 and ~1 month yr–1 (35 yr)–1 for fraction area and occurrence, respectively. The simulated tendencies in terms of fraction area of severe and extreme droughts agree well with observations. As seen from the spread of all models, the observed trend for severe drought occurrence is higher than inter-model spreads, while it is lower for extreme droughts.
The time series of four meteorological anomalies during four drought categories across global drylands for 1980–2014 are then presented in Fig. 10. In the observations, the three primary meteorological anomalies show an increasing trend, indicating a substantial alleviation of precipitation deficit while warmer temperatures and higher atmospheric evaporative demand intensify during all drought categories (red lines in Figs. 10a–l). This indicates that the increasing PET induced by global warming plays a dominant role in aggravating droughts in drylands. As the increased precipitation anomaly is partly offset by the elevated atmospheric evaporative demand, the water deficit conditions (P–PET) during droughts tend to increase only slightly (red lines in Figs. 10m–p).
Time series of meteorological anomalies during four drought categories area-averaged across global drylands over 1980–2014. The four columns are meteorological anomalies for mild (SPEI≤–0.5), moderate (SPEI≤–1.0), severe (SPEI≤–1.5), and extreme (SPEI≤–2.0) droughts, respectively. The four rows depict the anomalies of precipitation (units: mm month–1), temperature (units: °C), potential evapotranspiration (PET, units: mm month–1), and water balance (P–PET, units: mm month–1), respectively, relative to the 1980–2014 monthly climatology. Red and blue lines denote the observation and CMIP6 multi-model mean ensemble (MME), respectively, and grey shadings denote the inter-model ranges between minimum and maximum.
CMIP6 MME (blue lines in Fig. 10) basically capture the long-term trends in the four drought-related meteorological anomalies, with their inter-model ranges (grey shadings in Fig. 10) generally covering the observations (red lines in Fig. 10). Specifically, the simulated temperature (Figs. 10e–h) and PET (Figs. 10i–l) anomalies are relatively comparable with the observations, but the magnitudes of precipitation (Figs. 10a–d) and P–PET (Figs. 10m–p) anomalies are overestimated, especially after the late 1990s. Additionally, the inter-model spreads of precipitation and P–PET anomaly are larger than the other two elements.
We further compare the observed and simulated long-term tendencies in the four meteorological anomalies during the four drought categories over global drylands and seven sub-drylands in Fig. 11. The observed tendencies of temperature anomalies are on the low side of the multi-model spread, whereas that of the other three meteorological anomalies are on the high side, in line with the results in Fig. 10. Among the seven sub-drylands, the observed tendencies for the four meteorological anomalies mostly fall within the inter-model ranges, except for the precipitation and PET anomaly in the North African drylands. The inter-model spreads generally tend to enlarge with increased severity of drought. For the precipitation anomaly tendencies (Fig. 11a), the inter-model spreads over the three drylands in the Southern Hemisphere (southern American, southern African, and Australian drylands) are wider than that in the Northern Hemisphere (Central Asian, East Asian, and northern American drylands), while the temperature anomalies show contrasting features (Fig. 11b). For the long-term tendencies of P–PET anomalies during droughts (Fig. 11d), due to the offsetting nature of the increased precipitation and PET anomalies (Fig. 11a, c), the tendencies of P–PET anomaly of MME in most sub-drylands (except for North American and Australian drylands) are close to 0 for mild and moderate droughts, but increase for severe and extreme droughts. Most models underestimate the increasing (decreasing) trends of P–PET during droughts over global (Glb) and specifically North African (NAf) drylands [during severe and extreme droughts over East Asian (EAs) and North American (NAm) drylands].
Long-term tendencies for drought-related meteorological anomalies area-averaged across global drylands and seven sub-drylands during 1980–2014. (a) Precipitation [units: mm month–1 (35 yr)–1], (b) temperature [units: °C (35 yr)–1], (c) potential evapotranspiration [PET, units: mm month–1 (35 yr)–1], (d) water balance [P–PET, units: mm month–1 (35 yr)–1]. Box-whisker plots illustrate the minimum, first quartile, median, third quartile, and maximum of long-term tendencies derived from CMIP6 multi-models, with colors from shallow to deep representing mild (SPEI≤–0.5), moderate (SPEI≤–1.0), severe (SPEI≤–1.5) and extreme (SPEI≤–2.0) droughts, respectively. The asterisks and dots denote the observations and CMIP6 multi-model ensemble mean (MME), respectively. The seven sub-drylands are the same as in Fig. 5.