What will be the consequence of global warming on regional soil moisture at the end of the 21st century? The response found in the fifth Assessment Report (AR5) of Intergovernmental Panel on Climate Change is blurred by many uncertainties, even when the focus is on a single business‐as‐usual scenario for the projected concentrations of greenhouse gases. Such a confusion is dominated by climate model uncertainties on the long term but might be also due to internal climate variability on the near term. Here we use a detection‐attribution methodology to demonstrate that recent trends in soil moisture and in near‐surface relative humidity averaged over the boreal midlatitude continents in summer have been mainly driven by human activities. Then we show that there is a fairly strong relationship between the near‐term versus long‐term aridity response among a set of 20 climate models, thereby supporting the limited influence of internal climate variability on near‐term variability. Finally, we use this emergent relationship to constrain the long‐term model response with the recent trends estimated from the observations and find that the projected long‐term drying was probably underestimated by most global climate models explored in the AR5.

Early assessments of the hydrological impacts of global warming suggested both an intensification of the global water cycle and an expansion of dry areas. Yet these alarming conclusions were challenged by a number of latter studies emphasizing the lack of evidence in observations and historical simulations, as well as the large uncertainties in climate projections from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Here several aridity indices and a two‐tier attribution strategy are used to demonstrate that a summer midlatitude drying has recently emerged over the northern continents, which is mainly attributable to anthropogenic climate change. This emerging signal is shown to be the harbinger of a long‐term drying in the CMIP5 projections. Linear trends in the observed aridity indices can therefore be used as observational constraints and suggest that the projected midlatitude summer drying was underestimated by most CMIP5 models. Mitigating global warming therefore remains a priority to avoid dangerous impacts on global water and food security.

1 Introduction Water scarcity is a major threat for food security and economic prosperity in many countries, which is not expected to decrease given the growing global population and related pressure on available water resources. Moreover, the global water cycle might be seriously affected by the projected climate change due to anthropogenic emissions of greenhouse gases (GHGs). The threat of an increased risk of drought, including in the summer midlatitudes, was highlighted by both empirical (e.g., Dai et al., 2004) and numerical studies (e.g., Douville et al., 2002; Frierson & Scheff, 2012; Scheff & Frierson, 2015). Off‐line hydrological simulations suggest that a global warming of 2°C above present will increase the population living under extreme water scarcity by another 40% compared with the effect of population growth alone (e.g., Schewe et al., 2014). Nevertheless, such hydrological impacts are highly model dependent, with both global climate models (GCMs) and off‐line land surface models (LSMs) contributing to the spread. Internal climate variability is also a major source of uncertainty, even if the model formulation generally dominates the spread by the end of the 21st century (Orlowsky and Seneviratne, 2013). In contrast to the mainstream thinking, a number of studies cast serious doubts on the reality of the ongoing and/or projected global drying. First, empirical aridity indices must be interpreted cautiously since they rely on a simplified calculation of potential evaporation that may respond incorrectly to the land surface warming observed in recent decades (Joetzjer et al., 2013; Sheffield et al., 2012). Moreover, the general climate change paradigms that “dry regions are getting drier and wet regions are getting wetter” and that “warmer is more arid” have been recently challenged (Greve et al., 2014, Roderick et al., 2015). When aridity changes are assessed as the lack of precipitation, the lack of runoff, or using a carbon budget approach, most global model outputs suggest that “warmer is less arid” (Roderick et al., 2015). In addition, rising atmospheric CO 2 is likely to decrease the plant water use, a physiological process which is overlooked in many climate models and/or impact studies and which can reduce future drought stress (e.g., Best et al., 2007; Swan et al., 2016). Finally, Berg et al. (2016) found a robust vertical gradient of soil moisture anomalies in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models, with more negative changes projected near the surface, thereby suggesting that the surface drying predicted by empirical and/or off‐line metrics may tend to exaggerate changes in total soil moisture availability. The present study has a twofold objective: (i) attributing the recent variability and (ii) constraining the long‐term evolution of total soil moisture and related variables over the northern midlatitude continent in summer (June to September, hereafter JJAS). The first objective is achieved by analyzing ensembles of global atmospheric simulations driven by the human‐forced versus total variations of observed sea surface temperature (SST) and sea ice concentration (SIC). The second objective is achieved by using the diagnosed human‐forced aridity trends as an emergent constraint for the aridity projections derived from a subset of 20 CMIP5 models. Given the lack of direct total soil moisture observations at the global scale, an off‐line land surface reanalysis will be used as well as other (indirect) land surface aridity indices. Section 2 describes the experiment design and the observed data sets. Results are shown in section 3. A recent land surface drying is found in multiple data sets, which is dominated by anthropogenic climate change. The implications of this attribution for aridity projections are also discussed in section 3 and suggests that this emerging signal is the harbinger of a much stronger drying, which was underestimated by most CMIP5 models. The main conclusions are drawn in section 4.

2 Experiment Design and Data Sets With the increasing confidence that recent global warming is very likely due to human activities, detection and attribution (hereafter) D&A studies have recently moved from temperature to other climate variables equally relevant for impact studies. As far as the global water cycle is concerned, formal D&A studies have revealed a human influence on the zonal mean distribution of precipitation (e.g., Zhang et al., 2007) and surface evapotranspiration (e.g., Douville et al., 2012). Yet the net effect of such changes on the land surface water budget is difficult to assess. D&A of observed changes in continental waters remains a difficult challenge given the limited instrumental record and/or the direct human influence on rivers and reservoirs. To the author's knowledge, the recent study by Mueller and Zhang (2016) is the only one that has been so far successful at attributing changes in soil moisture. The focus was on the northern continents and on the hottest season. The analysis was limited to the 1951–2005 period and based on the comparison between soil moisture outputs from CMIP5 models versus LSMs. Here the focus is also on the northern midlatitude continents and on the boreal summer season, but the study uses one more decade of data and a two‐tier D&A strategy. First, a formal D&A algorithm is applied to isolate the SST and SIC variations driven by anthropogenic and/or natural radiative forcings (cf. supporting information (SI)). Then, ensembles of atmospheric‐only global climate simulations are performed to attribute recent changes in soil moisture and related land surface variables. So doing, the CMIP5 models are only used to assess the externally forced variations in the SST observations. By prescribing the observed SST variability, our strategy enables a more straightforward comparison with the observations (e.g., Dai, 2013). The experiment design is summarized in Table 1. Two ensembles of 1920–2014 global atmospheric simulations have been achieved. ALL is a nine‐member ensemble of extended AMIP (Atmospheric Model Intercomparison Project) simulations driven by observed SST/SIC and radiative forcings. ANT is a five‐member ensemble driven by the human‐forced SST/SIC variability and the anthropogenic radiative forcings (no volcanic eruption and no variation in solar activity). Note that two additional nine‐member ensembles have been achieved to isolate the role of the internal versus externally forced climate variability (cf. Table 1). No ensemble is available to diagnose the influence of natural climate variability (i.e., internal variability + natural forcings) so that this influence will be here estimated as the difference between the ALL and ANT ensembles. Table 1. Summary of the Ensemble AGCM Experiments Expt SST Radiative forcings Size ALL Observed AMIP NAT + ANT 9 EXT Externally‐Forced AMIP NAT + ANT 9 INT Internal AMIP Fixed 9 ANT Human‐Forced AMIP ANT Only 5 All experiments are based on version 6.2 of the ARPEGE‐Climat AGCM, which is an update of v5.2 as described in Voldoire et al. (2013). The main differences between v5.2 and v6.2 include a modification of the vertical diffusion scheme and a new shallow and deep convection scheme. The land surface model has been also improved in many respects, including the introduction of a direct (biophysical) CO 2 effect on stomatal closure (Joetzjer et al., 2015) and the representation of floodplains and groundwaters fully coupled to the soil hydrology. The groundwater scheme needs to be integrated for many decades to reach an equilibrium under preindustrial climate conditions. Such a strategy was not here feasible given the limited computing resources and the decision to start the simulations in 1920. The different members of our experiments were initialized at 5 year intervals from a first extended AMIP integration after a minimum spin‐up of 30 years. This is sufficient for most land surface variables to reach equilibrium. A residual drift of deep soil moisture is, however, discernible, which does not represent a major issue since the role of natural climate variability is assessed as the difference between the ALL and ANT ensembles (sharing the same drift). As far as the CMIP5 models are concerned, all results are based on a single realization of the historical (1850–2005) and corresponding RCP8.5 (2006–2100) simulations. Only a subset of 20 models (cf. Table S1 in SI) has been used to avoid or at least mitigate the issue of model interdependency when building emergent constraints on model projections (Knutti et al., 2017). The main selection criterion was avoiding the use of several models based on the same atmospheric component. A second criterion was the availability of monthly mean model outputs not only for total soil moisture but also for the following variables: near‐surface daily mean, daily minimum, and daily maximum temperatures (the latter two being used to assess the diurnal temperature range, hereafter DTR); near‐surface relative humidity (hereafter RH); and surface evaporative fraction (hereafter EF) estimated as the ratio between latent heat and total (latent + sensible) turbulent fluxes at the land‐atmosphere interface. The observational counterpart is based on the following data sets: the 1979–2010 European Centre for Medium‐Range Weather Forecasts Re‐Analysis Interim (ERAI)‐Land off‐line land surface reanalysis (Balsamo et al., 2015) for soil moisture, the 1979–2016 ERAI atmospheric reanalysis assimilating SYNOP observations (Dee et al., 2011) for RH and the global mean surface temperature (GMST), the 1901–2014 CRU_TS3.23 climatology (https://crudata.uea.ac.uk/cru/data/hrg/) for near‐surface temperature, and the 1982–2011 Model Tree Ensemble (MTE) upscaling of in situ FLUXNET measurements (Jung et al., 2010) for EF. In addition to CRU_TS3.23, Hadex2 (http://www.metoffice.gov.uk/hadobs/hadex2/) was also used as an alternative gridded DTR data set and led to similar results. Note that ERAI‐Land is a land surface model simulation driven by observed meteorological forcings. The water budget is thereby perfectly closed, making ERAI‐Land a more suitable soil moisture data set for climate applications than ERAI. Yet there is no soil moisture data assimilation so that it is important to use multiple aridity indices in the present study. Modern‐Era Retrospective Analysis for Research and Applications (MERRA)‐Land was not used as an alternative soil moisture reconstruction, since it shows less consistent trends than ERAI‐Land when compared with satellite surface soil moisture over recent decades (Albergel et al., 2013). Additional data sets could have been explored, such as surface soil moisture, vegetation indices, or precipitation, but they would have been unavailable for most CMIP5 models and/or less connected to total soil moisture. Maps of correlations of JJAS anomalies indeed suggest that RH (Figure S1) and DTR (Figure S2) are strongly linked to total soil moisture at interannual timescales in both observations and the CNRM AGCM. Moreover, the simulated correlations are quite realistic in the northern midlatitudes, suggesting that these variables can be here considered as consistent surrogates for soil moisture. In contrast, the correlation with EF (Figure S3) is stronger than inferred from reconstructions and does not show a clear transition between soil moisture versus energy‐limited evapotranspiration around 55°N, as suggested by the ERAI‐Land and MTE data sets. While this discrepancy might be the evidence of a too strong land‐atmosphere coupling (e.g., Levine et al., 2016; Vilesa et al., 2017), it might be also due to inconsistencies between the reconstructed soil moisture and EF, the latter being based on an empirical upscaling technique and a very limited number of in situ observations. As a result, and owing to the limited length of this data set, we will not use the MTE reconstruction to constrain the CMIP5 projections in the continuation of the study. Note finally that the ERAI‐Land reanalysis uses a four‐layer land surface model with uniform thicknesses, while soil depth is not uniform in most climate models and is highly model dependent in the CMIP5 archive. It is therefore necessary to standardize the soil moisture values for comparing the various data sets. This standardization (i.e., substracting the climatological mean and dividing by the climatological standard deviation) was here done after averaging the total soil moisture over the northern midlatitude domain (hereafter referred to as the SSM index). This choice avoids giving too strong an emphasis on small absolute soil moisture variations in arid areas and is more relevant to discussing the evolution of the regional land surface water budget.

3 Results Figure 1 compares global maps of 1979–2014 linear trends in JJAS RH between ERAI and three ensembles of global atmospheric simulations: CMIP5, the ALL ensemble driven by observed SST and radiative forcings, and the INT ensemble driven by the internal variability of the observed SST and fixed radiative forcings (1920–1960 climatology). In line with the results of Simmons et al. (2010) and with other data sets, ERAI shows large drying trends in many areas, including over most boreal midlatitude continents. This pattern is strongly underestimated by CMIP5 models although they also show consistent drying trends over North America and Europe. The ALL ensemble is globally more realistic even if the pattern shows some differences with ERAI. The INT ensemble hardly shows any trend in the midlatitudes, thereby suggesting that the drying simulated in the ALL ensemble is dominated by the external radiative forcings. Figure 1 Open in figure viewer PowerPoint Global distribution of 1979–2014 linear trends in JJAS surface air relative humidity (% per decade) in (a) the ERA‐Interim Reanalysis, (b) a subset of 20 CMIP5 models, (c) the ALL ensemble driven by observed SST and radiative forcings, and (d) the INT ensemble driven by the internal variability of the observed SST and fixed (1920–1960 average) radiative forcings. Hatching denotes consistent trends among the different members of the ensembles using a t test at a 5% level. The hypothesis of a recent summer drying hiatus in most CMIP5 models is further supported by Figure S4 which mainly shows time series of JJAS land surface anomalies averaged over the boreal summer midlatitude continents. Note that this underestimated drying has not much to do with the recent global warming hiatus which shows a distinct spatial and seasonal signature (Trenberth et al., 2014). Figure S4 also highlights the large spread found in the CMIP5 projections, especially for the SSM index and the related land surface variables. Even the sign of the response remains uncertain for DTR, EF, and SSM at the end of the 21st century. Such a dispersion raises the question of whether the recent model behavior is informative about their long‐term response and whether it can be assessed on the basis of a single simulation given the possible influence of internal climate variability. Before exploring this, Figure 2 compares the 1979–2014 linear trends between the ANT and ALL ensembles conducted with the CNRM AGCM to discuss the anthropogenic versus natural origin of the recent multidecadal variability. The influence of the natural climate variability can be estimated as the difference between the ALL and ANT ensembles. The underlying assumption is that the atmospheric responses to individual radiative and/or SST forcings are additive. While such a hypothesis cannot be verified on the basis of our experiment design (cf. Table 1), the additivity of the JJAS midlatitude anomalies is verified in the case of the INT and EXT additional ensembles (not shown). The only noticeable exception is the total soil moisture response, in line with the residual drift discussed in section 2. Given our initialization strategy, this drift is however common to all ensembles and is therefore suppressed when estimating the natural variability as the difference between ALL and ANT. Figure 2 Open in figure viewer PowerPoint (a) Simulated versus ERAI time series of global annual mean surface air temperature anomalies (°C) from 1960 to 2015. Other panels show simulated time series of JJAS mean anomalies averaged over the northern midlatitude continents [35–55°N] for (b) surface air temperature (°C) versus CRU, (c) diurnal temperature range (°C) versus CRU, (d) surface air relative humidity (%) versus ERAI, (e) evaporative fraction versus MTE, and (f) standardized soil moisture anomalies versus ERAI‐Land. For each ensemble (ALL in green and ANT in orange), the thick line denotes the ensemble mean, while the shaded area denotes the 95% confidence interval for the ensemble mean. Linear fits are estimated over the 1979–2014 period (except for shorter observational records). Vertical dashed lines denote JJAS seasons with major anomalies of global mean volcanic aerosol optical depth (in black) or Niño3–Niño4 SST (warm events in red and cold events in blue). R is the temporal correlation of the ensemble mean anomalies with the observed anomalies. Starting with the annual mean GMST response as an illustration, the global warming trend simulated in the ANT ensemble is slightly stronger than in the ALL ensemble (Figure 2a), thereby suggesting a recent global cooling due to natural climate variability. In line with Douville et al. (2015), this relative cooling is primarily due to internal climate variability (not shown) and is not found over the boreal midlatitude continents in summer (Figure 2b). More interestingly, the robust drying trend simulated in the ALL ensemble is mainly attributable to the anthropogenic forcings (Figure 2c–2f). While the negative trend in total soil moisture (Figure 2f) is robust in both ALL and ANT, the former experiment is less consistent with ERAI‐Land thereby suggesting that the CNRM AGCM slightly underestimates the “observed” midlatitude drying. Looking at the other components of the water budget (Figure S7), it seems that this feature is not due to an underestimation of the precipitation decrease but rather to the evapotranspiration trend (although the MTE reference data set does not cover the whole 1979–2014 period and the linear trends are not significant over such a short period). The robust summer midlatitude drying simulated in ALL and ANT is also found in RH (Figure 2d) and is quite consistent with ERAI after 1979. It is also consistent with a recent increase in the simulated DTR, which is, however, not found in the CRU data set. Yet a wider 1960–2014 perspective supports the trend reversal simulated at the end of the twentieth century in both ALL and ANT. This multidecadal variability is compatible with a dominant time‐varying radiative effect of anthropogenic aerosols (Boé, 2016; Zhou et al., 2010). Such a hypothesis is supported by the sliding temporal correlations shown in the supporting information. While both RH and DTR are robust proxies of the SSM index (Figure S5), they are also sensitive to the variability of downward surface solar radiation (Figure S6). This radiative signature seems quite realistic in the CNRM AGCM, at least for the soil moisture and RH indices which can therefore be used to constrain the CMIP5 projections. Figure 3 shows scatterplots of the long‐term RH and SSM responses versus the recent linear trends estimated over the 1979–2014 period. As indicated by the squared correlations in Figures 3a and 3c, such trends explain 24 to 60% of the intermodel spread at the end of the 21st century. The idea that climate change is emerging in the instrumental record and can be used to constrain the long‐term projections is not new (e.g., Knutti et al., 2017) but can be misleading if the observed trends are not dominated by anthropogenic forcings. Note that our CMIP5 ensemble (only one realization for each model) here samples both uncertainties in the model formulation and in the initial conditions. Yet internal climate variability can be considered as a random effect in these coupled simulations and has therefore a limited impact on the regression slope. Figure 3 Open in figure viewer PowerPoint (a) Scatterplot of 2071–2100 anomalies (%) versus 1979–2014 linear trends (% per century) of JJAS near‐surface air relative humidity averaged over the northern midlatitude continents among a subset of 20 CMIP5 models and related linear regression (red line, 95% confidence interval in shading). (b) Prior (no observational constraints) and constrained (see text for details) pdfs of the CMIP5 anomalies (%). (c, d) Same as Figures 3 a and 3 b but for standardized soil moisture (SSM in standard deviation). In panels Figures 3 a and 3 c, vertical lines and the associated 95% confidence intervals in shading denote the observed linear trend (black, only over 1979–2010 for SSM), the ensemble mean trend simulated in ALL and ANT (green and red), and the observed linear trend attributed to anthropogenic forcings (maroon). All anomalies are derived from a unique realization of the RCP8.5 scenario and estimated against the 1971–2000 climatology from the corresponding historical simulation. More critical is the potential role of natural variability in the observed trends if one plans to use them as observational constraints. In both Figures 3a and 3c, the observed trends and their likely range (95% confidence interval) are shown as black lines and gray shadings, respectively. Also shown are the corresponding trends and confidence intervals in the ALL (green) and EXT (red) experiments. Not surprisingly, given the contrasted fraction of the intermodel spread explained by the linear regressions, the confidence intervals are tighter and the difference between ALL and ANT is smaller for the SSM compared to RH index. Arguing that the human‐induced linear trends represent a better constraint on the long‐term response of the CMIP5 models than the observed trends, the difference between ALL and ANT can be used to translate the observed trends on the x axis as represented by the maroon lines compared to the black lines. The confidence intervals are then also increased to account for the uncertainties in the ALL minus ANT differences. Figures 3b and 3d illustrate the distribution of the CMIP5 model uncertainties and their potential reduction through the use of the emergent constraints shown in Figures 3a and 3c, respectively. The prior distribution (in red) is assumed to be Gaussian and is only estimated from the discrete realizations of the CMIP5 models. Using the observed 1979–2014 linear trends to constrain the projections through a simple calculation of conditional probabilities leads to a steeper distribution (in black) with a tighter 95% confidence interval (shading) and a drier ensemble mean response for both RH and SSM. This shift and narrowing is more important for soil moisture than for RH raising some questions about the consistency between both indices. Yet there is a qualitative agreement between the two diagnostics whereby the summer drying of the northern midlatitude continents was underestimated by most CMIP5 models. Taking into account the additional constraint about the limited contribution of natural climate variability to the observed trends (cf. maroon pdfs) does not change this conclusion which is even slightly reinforced in the case of total soil moisture.

4 Summary Several surface aridity indices have been explored in both CMIP5 models and global observations or reconstructions to assess trends over recent decades and their possible use as emergent constraints on the long‐term projections. Focusing on the boreal summer midlatitude continents, the results suggest that such a strategy could be particularly efficient to constrain the late 21st century response of total soil moisture in RCP8.5 scenarios, provided the availability of reliable observations over three to four decades. In the lack of direct observations, the ERA‐Interim land surface reanalysis suggests a strong underestimation of the recent and future soil drying in the CMIP5 models. This result should be considered with caution given the nature of the ERAI‐Land reanalysis (i.e., an off‐line land surface simulation without data assimilation). Nevertheless, it is supported by a similar analysis based on near surface relative humidity and on more reliable observations given the assimilation of conventional synoptic measurements in the ERAI data set. In this case, the emergent constraint is, however, less efficient given the weaker link between recent and future RH changes and/or the stronger role of internal climate variability in the CMIP5 models. Moreover, results obtained with the diurnal temperature range (DTR, cf. Figures S7c and S7d) suggest a slight overestimation of the projected DTR increase in the CMIP5 models which is in apparent contradiction with the underestimated drying. While this paradox might be explained by atmospheric radiative processes, we cannot totally exclude that the surface drying found in both ERAI and ERAI‐Land is somewhat overestimated. Further studies should clarify whether this midlatitude drying is dominated by a decrease in precipitation and/or an increase in surface evapotranspiration. While Figure S7b shows a decrease in simulated precipitation, the trend is neither robust (i.e., data dependent) nor statistically significant in the observations. Reconstructions of global evapotranspiration are even more uncertain, and, although Douville et al. (2012) suggested a human‐induced increase in the midlatitude evapotranspiration since the 1960s (cf. their Figure 1), such a result needs to be confirmed with new data sets extended to recent years. Moreover, the relative influence of changes in evapotranspiration versus precipitation might be different between the early and late 21st century. While the boreal midlatitude summer warming might first increase surface evapotranspiration (E) without increasing precipitation (P), the induced soil drying might ultimately lead to a decrease in both P and E or at least to a weaker increase in E. This negative soil moisture feedback on E can explain why the emerging drying trend (per century) is stronger than the long‐term drying (slope < 1) in the scatterplots of CMIP5 models shown in Figure 3. Clearly, our results support the use of multiple metrics to constrain global climate projections (Knutti et al., 2017). But they also suggest that the end of model democracy is not straightforward as long as the relative merits of different metrics are not considered, both in terms of physical relevance and of observational uncertainty. The emerging climate change signal in the instrumental record makes the use of observed trends more and more attractive for constraining the multi‐model response but might be misleading if one does not care about the anthropogenic origin of the trends. In line with Mueller and Zhang (2016), our study suggests that the recent boreal midlatitude summer drying was mainly caused by human activities, thereby supporting the use of observed trends to constrain the CMIP5 projections. Yet beyond the two‐tier strategy proposed in the present study and with the forthcoming availability of CMIP6 historical simulations, further work is probably needed to take advantage of formal D&A tools in the development of observational constraints at both global and regional scales.

Acknowledgments The authors would like to thank all people at CNRM who are involved in the development of the ARPEGE‐Climat and SURFEX models. Thanks are also due to Aurélien Ribes (for the breakdown of the observed SST variability) and Sophie Tyteca (for her efficient technical support) at CNRM, as well as to the anonymous reviewers for their useful comments. This work was supported by the French ANR MORDICUS project (ANR‐13‐SENV‐0002 contract). All the monthly mean prescribed SST and CNRM model outputs can be downloaded from the CNRM anonymous ftp server upon request.

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