An uncertain future change in aridity over the tropics

We assess the effects of climate change using models that participated in CMIP6, using the historical emission scenario (Eyring et al 2016), the SSP2-4.5 emission scenario (a low-medium emission scenario for which global mean radiative forcing is designed to reach 4.5 W m⁻² at the end of the 21st century; O'Neill et al 2016), and single forcing experiments of the Detection and Attribution MIP (DAMIP; Gillett et al 2016). We used the SSP2-4.5 emission scenario which is the emission scenario that was used for the DAMIP simulations.

The single-forcing simulations are used to quantify the effects of each individual forcing on climate. In the single-forcing experiments all external forcings are kept constant at pre-industrial values, with the forcing of interest changing with time, following historical emissions from 1850 to 2020 and the SSP2-4.5 emission scenario thereafter:

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  The AA-only simulations (hist-AER and SSP245-AER) resemble the historical and SSP2-4.5 simulations but are forced by changes in AA forcing only [black carbon, organic carbon, SO₂, SO₄, NO_x , NH₃, CO, non-methane volatile organic compounds (NMVOCs)]. Changes in AA emissions are heterogeneous, with for instance the emissions of SO₂ decreasing over the US, Europe, and China, and increasing over Africa and India (figure S1).
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  The GHG-only simulations (hist-GHG and SSP245-GHG) resemble the historical and SSP2-4.5 simulations but are forced by changes in GHG only.
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  The natural-only simulations (hist-NAT and SSP245-NAT) resemble the historical and SSP2-4.5 simulations but are forced with only solar and volcanic forcings.

We use the outputs of the three climate models that performed the future projections of the DAMIP experiments, and that provided at least 5 ensemble members to provide an estimation of the effect of internal climate variability and of the externally forced response. The number of models employed for future single-forcing comparisons is therefore limited by the lack of provision of suitable ensemble sizes by other modeling groups. We use 10 ensemble members each for CanESM5 and MIROC6 and 5 ensemble members for GISS-E2-1-G (table S1). All simulations are interpolated onto the same horizontal resolution, using a 2.5° regular grid for surface air temperature and precipitation.

We compare results of the three models we used here to an ensemble of 26 CMIP6 models, using the historical and SSP2-4.5 emission scenarios (table S2). The ensemble of the three climate models that provide future changes of the DAMIP experiments is hereafter called DAMIP, while the ensemble of the 26 CMIP6 models is hereafter called CMIP6.

2.2.1. The climate moisture index (CMI) index

We use the CMI (Willmott and Feddema 1992), an indicator of the potential water availability, which was previously used to document climate change risks and changes in land aridity (e.g. Bonfils et al 2020). CMI depends on precipitation and PET and is calculated as follows:

$$\begin{align*}{\text{CMI}} = \left\{ \begin{array}{*{20}{c}} {\frac{P}{{{\text{PET}}}} - 1{\text{ if }}P < {\text{PET}}} \\ {1 - \frac{{{\text{PET}}}}{P}{\text{ if }}P \unicode{x2A7E} {\text{PET}}} \end{array}\right.,\end{align*}$$

where P is precipitation and PET. PET is calculated using the Penman–Monteith Equations following the Food and Agriculture Organization recommendations (Allen et al 1998) (see the method section of the supplementary material and table S3). An increase in atmospheric CO₂ concentration drives partial stomatal closure, reducing evapotranspiration (Lemaitre-Basset et al 2022). We then account for the effect of the vegetation response to an increase in CO₂ concentration, following Yang et al (2019). See explanation in the supplementary material. Accounting for the vegetation response reduces the future change in PET but does not significantly affect the changes in aridity (figure S2).

CMI is calculated using monthly values prior to computing the seasonal mean. CMI ranges from −1 to +1, with positive values indicating a wet climate, which is when precipitation exceeds PET. CMI is positive during the monsoon season and is negative in winter. A negative value of CMI indicates a dry climate. A change in the value of the CMI will affect the probability of drought and water crisis—a change in sign is not required for a large societal impact.

2.2.2. The effect of external forcings

2.2.3. The monsoon domains

2.2.4. Assessing robustness in the effects of the external forcings

We define the effects of the externally forced response as robust when the signal-to-noise ratio (S/N) is greater than one, with

$$\begin{align*}{\text{S}}/{\text{N}} = \frac{{\overline {\Delta v} }}{{{\sigma _{\Delta v}}}}\end{align*}$$

where $v$ is a given variable, $\Delta v$ indicates the future change of $v$, the overbar denotes the ensemble mean and $\,{\sigma _{\Delta v}}$ is the standard deviation of the change of a given variable, computed across ensemble members. We assume that $\overline {\Delta v}$ is an estimate of the effects of the externally forced response because the ensemble mean removes the effects of internal variability that exists in each simulation but that are unlikely to be in phase (Deser et al 2014).

We compute S/N for each model separately, using all ensemble members (e.g. 10 ensemble members for CanESM5). For each model, ${\sigma _{\Delta v}}$ documents the deviation from the effects of the externally forced response, and we assume that it provides an estimate of the effects of internal variability. A S/N value greater than unity then indicates that the effects of the externally forced response exceed the estimate of the effects of internal climate variability, hence highlighting the robust effects of the externally forced response, relative to internal climate variability.

We also compute S/N using all available ensemble members together (i.e. 35 ensemble members, with 10 from CanESM5, 5 from GISS-E2-1-G, and 10 from MIROC6). When computed from the ensemble members of all models, an S/N value greater than unity indicates that the estimate of the externally forced response is greater than the combination of the model uncertainty and the effects of internal climate variability.

In addition to the S/N ratio we quantified the significance of the anomalies using a Student's t test. We show that the quantification of the robustness of the results is only moderately impacted by the metric we use (figures S3 and S4).

2.2.5. The indices

Surface air temperature is expected to increase towards the end of the 21st century due to anthropogenic activity (figure 1(a)). The DAMIP experiments show that the increase in global mean surface air temperature (GMST) is primarily driven by increasing GHG concentrations (figure 1(c)). The AA effect also contributes to the increase in GMST (figure 1(b)) through allowing an increase in surface downwelling shortwave radiation as aerosol emissions reduce (figures S1 and S5). The increase in surface air temperature due to aerosol reductions is robust almost everywhere, except over the North Atlantic subpolar gyre. Here, the change in surface air temperature is uncertain, as seen in Lehner et al (2020), and which we hypothesize to be due to changes of the oceanic circulation (Bellomo et al 2021). We show that PET increases, globally, in a warmer climate, consistent with the literature (e.g. Greve et al 2019) (figure S6).

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Changes in regional precipitation are more uncertain than changes in surface air temperature, as shown by there being fewer regions of the globe where S/N values exceed unity (figures 1(a) and (e)). Precipitation increases over the tropics, with the exception of central and SAM, where precipitation decreases due to the externally forced response (figure 1(e)), in agreement with Chen et al (2020). Changes in precipitation are primarily contributed by the GHG effect (figure 1(g)). Changes in aerosol emissions over the 21st century are associated with an increase in precipitation over the tropics, but with low robustness across the ensemble (figure 1(f)).

Changes in the CMI index follow the change in precipitation and are more uncertain than the changes in surface air temperature (figures 1(a) and (i)). The central Sahel and India are projected to become wetter, while aridity increases over central and SAM, western Sahel, southern Africa, and East and Southeast Asia (Figure i), in agreement with previous studies (Asadi Greve and Seneviratne 2015, Zarch et al 2017, Greve et al 2019). Like precipitation, the change in CMI is primarily contributed by the GHG effect (figure 1(k)), while the AA effect is weaker over the monsoon regions in the long term (figure 1(j)).

In contrast to the GHG and AA effects, NAT effects do not contribute to the change in surface air temperature (figure 1(d)), precipitation (figure 1(h)), and aridity (figure 1(l)).

We expect the relative importance of the GHG and AA effects on the full change in aridity to be dependent on the timescale of interest. We show that the AA effect is greater than the GHG effect over the Sahel for the near-term horizon (2021–2040), and of the same importance as the GHG effect over India (figure S7). However, changes in aridity are small globally for the near-term horizon (figure S8), with fewer regions in which S/N exceeds unity (figure S7). Therefore, we focus the remaining analysis on the long-term change in aridity.

We show changes in CMI, averaged over each monsoon domain and in response to changes of all forcings (figure 2), for long-term projections, and for both CMIP6 and DAMIP ensembles. The CMIP6 ensemble projects an increase in aridity over the NAM, SAM, SAF and AUS monsoon domains, with a wetter climate over the SAS monsoon domain and with no change over the NAF and EAS monsoon regions (figure 2(a)). The DAMIP ensemble shows the same behavior in changes in aridity (figure 2(b)), showing that it provides a good representation of the CMIP6 ensemble. There is a large diversity of responses among the three climate models, both in terms of magnitude (e.g. for the SAM summer monsoon precipitation) and sign of the change (e.g. NAF summer monsoon precipitation). The strongest differences between models are obtained over the NAM, SAM, and NAF monsoon domains, and for the EAS monsoon domain (figure 2(b)).

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CanESM5 is the most sensitive to GHG, simulating the strongest changes in aridity relative to the CMIP6 ensemble (figures 2(a) and (b)). We show this to not solely due to a greater warming in CanESM5, as scaling the changes in aridity by changes in GMST does not alter the results (figure S9). We thus suggest that one relevant explanation for the differences between climate models at simulating the changes in CMI is the difference in the patterns of change in surface air temperature and atmospheric circulation.

The AA effect is associated with a small change in the CMI, and the ensemble spread is low (figure 2(c)). In contrast, GHG effects largely explain the future change and ensemble spread in the aridity index (figure 2(d)). The NAT effect does not lead to robust changes in aridity over the monsoon domains (figure 2(e)), consistent with results of figure 1, because of the considered time scale (20 years) and because there are no strong changes in the solar forcing and the volcanic emissions. The same conclusion also holds for the near-term horizon (figure S8), with strong differences between climate models primarily arising due to the GHG effect.

The GHG effect explains the differences between models in simulating the long-term change in aridity. Thus, we focus on GHG effects on surface air temperature, precipitation, and aridity in each model. We focus on the long-term projection to capture the largest possible externally forced signal.

CanESM5 simulates a stronger increase in GMST and a stronger land-ocean thermal contrast than MIROC6 and GISS-E2-1-G (figures 3(a)–(c)). More specifically, a striking difference is obtained over the extratropical North Atlantic Ocean, with CanESM5 simulating a strong and robust warming, MIROC6 simulating a weaker warming, and GISS-E2-1-G simulating a robust cooling of the extratropical North Atlantic (figures 3(a)–(c)).

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CanESM5 also simulates stronger changes in precipitation than the other models globally (figures 3(d)–(f)). Strong differences are obtained regionally, with CanESM5 producing a strong robust decrease in precipitation over central and SAM, while changes are moderate and not robust in MIROC6 and GISS-E2-1-G (figures 3(d)–(f)). Models show a large diversity in response over northern Africa, with CanESM5 simulating a robust increase in precipitation, MIROC6 simulating a zonal contrast with a decrease in precipitation over the central Sahel and an increase over the western Sahel, as seen in a majority of CMIP5 and CMIP6 models (Monerie et al 2020), and GISS-E2-1-G producing a large-scale decrease in precipitation (figure 3(f)). There is more consistency in the sign of change over eastern India.

The change in aridity (figures 3(g) and (i)) follows the change in precipitation (figures 3(d)–(f)) over the monsoon regions, showing a divergence between models at projecting the long-term change in aridity, and suggesting that the change in precipitation controls the change in aridity.

We now assess mechanisms that explain the differences between climate models at simulating the GHG effect on aridity over land.

Precipitation increases over most of the monsoon domains and decreases over the NAM and SAM monsoon domains (figure 4(a)). Unlike precipitation, PET increases globally (figure 4(b)). The ensemble spread is higher for the change in precipitation than in PET, consistent with figure 3, suggesting that the change in precipitation explains the uncertainty at simulating the change in aridity. The ensemble spread is high though for the change in PET over the NAM and SAM monsoon regions (figure 4(b)), suggesting that the differences between models at simulating PET contribute to the uncertainty in the change in aridity (figure 2(b)). However, comparison of the magnitude of the ensemble-spread of the change in precipitation and PET shows that the key for understanding the future change in aridity lies in the future change in precipitation. We then focus the analysis on known drivers of summer monsoon precipitation change. Following section 3.3 we focus the analysis on the GHG effect.

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We first focus on a large-scale picture of the monsoons, tracking the meridional shift of the ITCZ and the large-scale temperature gradient. The ITCZ shifts northward in both MJJAS (figure 4(c)) and NDJFM (figure 4(d)), consistent with a strengthening of the ITD (Cao et al 2020). These large-scale changes are consistent with the changes of monsoon precipitation, locally, with a northward shift of the ITCZ associated with an increase in precipitation over the NAF, SAS and EAS monsoon domains, and with a decrease in precipitation over the NAM and SAM monsoon domains. Besides, the more pronounced change in ITD and location of the ITCZ in CanESM5 than in the other models is consistent with the stronger changes in precipitation in that model.

In addition to the large-scale temperature gradient, we show the change of the cross-equatorial Atlantic temperature gradient (NA/SA; figures 4(c) and (d)), both having effects on West African (Hoerling et al 2006, He et al 2023) and South American monsoon precipitation (Joetzjer et al 2013, Wang et al 2020). The change of the NA/SA temperature gradient is model dependent, with CanESM5 projecting a strengthened cross-equatorial Atlantic temperature gradient while GISS-E2-1-G projects a weakening of the NA/SA temperature gradient. This is consistent with the change in NAF monsoon precipitation, positive in CanESM5 and negative in GISS-E2-1-G. MIROC6 produces a moderate change of both NAF precipitation and NA/SA temperature gradient. This relationship between NA/SA and NAF precipitation is also obtained with the larger CMIP6 ensemble (full response to climate change), with a correlation coefficient between both indices of r = 0.76. These results therefore reveal a strong control of the change in subtropical North Atlantic temperature on future changes in West African precipitation and aridity.

In addition to the ocean temperature, the SAH/SA temperature gradient also strengthens more strongly in CanESM5 than in the other models (not shown), potentially indicating a strong effect of differences in land warming on the NAF summer monsoon precipitation, as shown in Hall and Peyrillé (2006), among others.

Changes in summer monsoon precipitation are also associated with changes in Pacific SSTs (e.g. ENSO, Pacific Decadal Oscillation), globally. Here, we show that the tropical Pacific warms more in CanESM5 than in GISS-E2-1-G and MIROC6 (figures 3(a)–(c)), but we found no evidence showing that changes in the Pacific temperature gradients (e.g. the equatorial Pacific zonal temperature gradient; Watanabe et al 2021) could explain the differences we show between models at simulating the future change in summer monsoon precipitation and aridity (figure S10).