Climate change projections

A hierarchy of models (i.e., global and regional climate models) with varying model complexity (e.g., process representation and spatial resolution) is typically applied to predict weather and climate. First, general circulation models (GCMs) simulate the global climate response to increasing greenhouse gas concentrations in three dimensions (i.e., several atmospheric layers and a lateral grid of200to 600 km). GCMs have considerably increased in complexity in the last decades and nowadays consider the complex coupled processes of several components, the atmosphere, hydrosphere, cryosphere, land surface, and biosphere, represented by various equations. For instance, global ocean and atmospheric circulation are represented within GCMs employing the Navier–Stokes equations subjected to external energy sources. In addition, some sub-grid processes that can not be resolved discretely within GCMs are typically accommodated empirically through parameterization, such as moist convection. However, due to the substantial computational demands of global approaches, GCMs can only simulate the climate on a coarse grid. Therefore, they may omit local-scale processes, such as orographic precipitation. As a result, GCMs perform reasonably well in simulating annual or seasonal averages of climate variables at large spatial scales. However, they can not provide reliable projections of local daily precipitation (Bates et al., 1998). In addition, local climate projection data from GCMs may be subject to

biases that may produce systematic errors when applied directly for adaptation planning and decision-making applications (e.g., Fowleret al., 2007; Snoveret al., 2013; Wigley et al.,1990), such as when applied to hydrological-hydrogeological models. For instance, recharge in karst terrains is considerably driven by event-based rainfall, necessitating at least daily precipitation data.

In this context, the two downscaling methods, statistical and dynamical down- scaling, are commonly applied to project the regional high-resolution (temporal and spatial) climate based on the GCMs. The term statistical downscaling describes the procedure of refining projections with the help of present regional distribution patterns of the climate variables. On the contrary, the dynamic downscaling of the climate projections are achieved by employing physics-based regional climate models (RCMs). RCMs are local models of the climate that commonly apply the result from GCMs as boundary conditions, enabling the dynamic downscaling of the GCM climate projection. However, due to the smaller domain, RCMs may apply much more resolved grids (i.e., 3 to 20 km) and, thus, accommodate local features, such as topography and surface waters, better. In addition, RCMs may also contain higher degrees of process representation due to lower computational demands. In summary, statistical downscaling methods can perform nearly equally well as dynamical downscaling on present-day climates (e.g.,Le Roux et al., 2018). However, the assumed statistical relationships may be invalid for novel conditions because of climate change. In comparison, dynamical downscaling methods employ physical principles that are expected to remain valid under climate change (e.g., Dixonet al.,2016;Lanzante et al.,2018).

The climate projections from above are based on various greenhouse gas concen- tration trajectories, corresponding to different climate forcing (Meinshausen et al.,2020;

Mosset al.,2010). For instance, the 5th assessment report of the Intergovernmental Panel on Climate Change (IPCC,2013) introduces the concept of representative concentration pathways (RCP), defining the four scenarios, RCP2.6, RCP4.5, RCP6.0, and RCP8.5, that correspond to different climate forcings of2:6,4:5,6:0, and8:5 W m⁻², respectively, in the year 2100. Here, the RCP2.6 scenario depicts a stringent scenario with instant mitigation measures to reduce greenhouse gas emissions, i.e., achieving peak emissions in the year 2020.

The scenarios, RCP4.5 and RCP6.0, can be considered as intermediate scenarios, reaching peak emission around the year 2040 and 2080, respectively. The RCP8.5 scenario assumes the worst-case with a continuous increase of greenhouse gas emissions. The most recent assessment report (IPCC,2021a) further supplements the RCP scenarios with the shared socioeconomic pathways (SSP), taking geopolitical aspects into consideration. However, this thesis applies the RCP scenarios from the 5th assessment.

Similar to groundwater modeling (see Chapter 2.1.3.3), climate models experience several sources of uncertainty. Hawkins &Sutton (2011) and Yipet al. (2011) specify

three broader sources of uncertainty in global and regional projections of decadal mean precipitation, model uncertainty (e.g., imperfect representation of the processes that determine the climate), scenario uncertainty, and internal climate variability, and quantify their relative importance depending on the projection time. The latter describes the natural variability of the climate without external forcing. Uncertainty is measured here as the disagreement among the ensemble projections and decomposed into the sources of uncertainty from above. They conclude that for the near future (i.e., for lead times up to 3 decades), internal climate variability and model uncertainty are the dominant sources of uncertainty. However, for more distant projections, the analyses suggest that internal climate variability becomes less dominant, and model uncertainty is likely the dominant source of uncertainty, followed by scenario uncertainty. Consequently, studies on the impacts of precipitation changes should consider various climate projections and scenarios to accommodate model and scenario uncertainty. Moreover, extreme precipitation exhibits considerable uncertainty in climate projections, further emphasizing the need for ensembles of climate projections. For instance,Kimet al. (2020) concluded that dry regions exhibit relatively higher uncertainty of daily precipitation than wet regions, making climate change impact assessments on the water budget, particularly for these regions, challenging.

50 0 50

SSP1-2.6: SSP2-4.5:

100 0 100

50 0 50

SSP3-7.0:

100 0 100

SSP5-8.5:

40 20 0 20 40

Change of precipitation (%)

Figure 2.7: Global ensemble projection of long-term average precipitation under the climate change scenarios, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, in 2081-2100 as compared to the reference period 1981-2010

(climate data obtained fromIPCC,2021b)

In the Mediterranean Basin, climate models commonly indicate that mean annual precipitation will decrease significantly (IPCC, 2021a). For instance, IPCC (2021b) analyzed up to 33 climate models to generate an ensemble prediction indicating with high confidence a median decline in annual precipitation of −2:7,−7:7,−15:2, and−18:8 %,

respectively, for the greenhouse gas emission scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 until the year 2100 (see Fig. 2.7). Moreover, the median consecutive dry days (CCD) is expected to increase by 6:3,10:3,15:4, and20:8 days, respectively, suggesting a shortening of the recharge-effective rainy season during the winter. At the same time, IPCC(2021b) points out with medium confidence that maximum diurnal precipitation may increase by circa 3:9to6:9 %. The climate shifts from above have unidentified implications for groundwater recharge and, therefore, groundwater availability in the region.