3.1 Annual Cycle
3.1.1 Current climate from the observation ensemble
Figure 2 shows the spatially averaged annual precipitation cycle for all models and the observation ensemble. Observations illustrate the aridity gradient with decreasing latitude in continental Chile. The arid Northern Chile subregion receives a median precipitation of 0.16 (Inter-Quartile range, IQR=0.18) mm/day, most of which falling in the months of December, January, and February, with amounts greater than 0.33 mm/day (75th percentile of the annual cycle). Further south, winter precipitation dominates the annual cycle of Central Chile and Northern Patagonia, where the months of May, June, and July make the largest contribution to the annual precipitation. However, the magnitude of precipitation is greater in Northern Patagonia, which receives about three times more precipitation than its northern neighbor (2.67 (IQR=1.73) vs 0.91 (IQR=1.39) mm/day). In Southern Patagonia, precipitation abounds all year around, with a median of 2.79 (IQR=0.25) mm/day. Here, maximum precipitation occurs during March, April, and May, which receive more than the 75th percentile (2.91 mm/day) of precipitation during the annual cycle (Figure 2-d).
Figure 3 evidences that the annual mean temperature ranges from 11.32 °C in Northern Chile to 6.23 °C in Southern Patagonia. During December, January, and February when the increased insolation in summer warms the land in all subregions mean temperature varies from 14.26°C in Northern Chile to 9.57°C in Southern Patagonia. Northern Chile exhibits slightly lower temperatures in austral summer than Central Chile (-1.67°C), which may be due to much higher mean elevation of this region (Figure 1). Beside the more poleward position of Northern and Southern Patagonia with respect to northern and Central Chile, lower temperature over this region is also influenced by the summer increase in onshore moisture transport described by Garreaud et al., (2013), which advent cool air over land. During the winter months the mean temperature varies from 7.95°C to 2.16°C between the Northernmost and the southernmost regions. Despite observations show no W-E gradient for temperature in Patagonia, precipitation displays a strong meridional asymmetry with maximum precipitation along western Southern Patagonia. This is shown in Figure 8-a, where a band of maximum annual precipitation stretches between parallel 47°S and 55°S and between meridians 72.5°W and 77°W. Precipitation rapidly decreases towards the east to a minimum of 0.57 mm/day in Tierra del Fuego.
In order to evaluate the dispersion between the observations over each subregion we used the Probability Density Functions (PDFs). The results are presented as density boxplots for a set of ranges of precipitation and temperature in Figure 4 and Figure 5, respectively (original PDFs can be seen in the Appendix). We detected substantial differences among observations in the distribution of precipitation in the wetter subregions of Northern and Southern Patagonia. The frequency density of the months with low precipitation (≤ 2mm/day) is much greater in CMAP than in the other products. PERSIANN exhibits the highest frequency of months with 2-4mm/day for the same subregions and in the Southern Patagonia it also shows the highest frequency for months with intensity ≥4mm/day (Figure 4b-c). CMAP and PERSIANN are the datasets showing the highest frequency of low-intensity months (≤ 2mm/day) for Central Chile, although in general the observations are relatively more homogeneous in the representation of low and medium-intensity precipitation months in this semi-arid region than in Patagonia (Figure 4-b). In Northern Chile, where the monthly precipitation amount rarely exceeds 2 mm/day, CHIRPS and CMAP showed the greatest frequency and greatest IQR of the months with low intensity (<2mm/day).
The monthly distribution of temperature presents more agreement among observations than the precipitation in all regions Figure 5. In the two northernmost regions the UDelaware dataset shows greatest frequency for the months with lowest temperatures (<5°C) and smallest frequency for the rest of the months with respect to two other datasets. Torrez-Rodriguez et al (2023) showed that CR2 is substantially warmer than CRU and UDelaware over high mountains between 25° and 35°S dataset. Our results suggest that CR2 dataset exhibits highest frequency of the temperature values in the range 10-15°C (Figure 5-a), but in terms of the PDF shape the CR2 is very similar to the CRU in all subregions (Figure S2).
3.1.2 CMIP6 ensemble versus observations
In Figure 6, we summarize the results of subregional differences in the annual cycle between CMIP6 models and the observation ensemble for the period 1986-2014 using R, MBE, and NRMSE (a detailed version of this summary is listed in Table A1 of the Appendix). We further used Taylor Diagrams to illustrate the R and standard deviation differences between GCMs and Observations (Figure 7). In general, CMIP6 captures the seasonal precipitation and temperature phasing reasonably well. However, our results show a high intra- and inter-subregional variability (summarized in Figure 6) with a marked overestimation of mean values in both fields across all subregions (Figures 2,3,6). In Southern Patagonia, the precipitation pattern is displaced by one month (Figure 2-d), showing the lowest correlation coefficient among all subregions (median = 0.54, Figure 6-a). In other subregions, CMIP6 can replicate very realistically the phase of the annual cycle of precipitation (R ≥ 0.95). The magnitude and direction of the error bias are shown by the mean bias error (MBE). This metrics, however, needs to be interpreted alongside NRMSE, given that the total precipitations amount varies significantly among the different subregions. The second lowest MBE occurs in Northern Chile (extreme arid) with a median of 0.44 mm/day. However, this subregion shows the greatest relative error as shown by the NRMSE with a value of 2.17 (205%). Central Chile presented the lowest NRMSE of 0.44 (37%), followed by Northern Patagonia with 0.46 (42%). Southern Patagonia showed the greatest MBE of 1.66 mm/day and the second largest NRMSE of 0.66 (65%).
Figure 6 further evidences that CMIP6 replicates the annual temperature cycle, and all subregions show a high correlation with the observation ensemble mean (R ≥ 0.96). Northern and Central Chile presented the highest MBE with 2.6°C and 2.1°C, respectively. This error decreases with latitude down to 0.6°C in Southern Patagonia. However, projections of GCMs are highly variable as shown by an IQR of 1.57°C (Figure 3). NRMSE registered values less or equal to 0.2°C across subregions.
Even though the pattern of the annual precipitation cycle is reasonably well captured by the GCMs and their ensemble (Figure 2 and Figure 3), they underperform variability. Taylor Diagrams of Figure 7 show that CMIP6 models have difficulties in replicating the amplitude of the precipitation annual cycle in all subregions. This is particularly true for Northern Chile, where models’ standard deviations can differ from the standard deviation of the observation ensemble by many orders of magnitude. This exaggeration of the amplitude of the annual cycle also occurs in the other subregions, with Southern Patagonia showing the most significant deviation from observations when all metrics are considered. The opposite happens with temperature, which underrepresents the annual cycle amplitude for all subregions (Figure 7).
3.2 Spatial pattern of annual means: CMIP6 ensemble bias
The spatial bias of the annual mean for precipitation and temperature of the CMIP6 ensemble is portrayed in Figure 8 and Figure 9, respectively. Figure 10 exhibits the latitudinal averages of temperature and precipitation of the CMIP6 and observational ensembles. CMIP6 precipitation shows a strong wet anomaly reaching up to 6.8 mm/day in Southern Patagonia and up to 5.8 mm/day in the southern tip of Northern Patagonia. A wet anomaly is present in Northern Chile, which shows a maximum of 4.5 mm/day towards the high Andean Plateau in Bolivia. Central Chile registered the lowest precipitation anomaly for Chile with values less than 1 mm/day. Central Chile is the subregion where CMIP6 best performs, as it shows the best spatial and temporal fit with respect to the observations (Figure 8,10). This result, though, does not extend to near-surface temperature as it shows the second largest positive anomaly with 7°C in high-elevation areas of the Andes Mountains, certainly due to an unresolved topography by coarse-resolutions CMIP6 models. This anomaly is only surpassed by 8°C in specific areas in Atacama Desert in Northern Chile. In the remaining subregions, temperature anomaly ranges within ±2°C, except for a slight cool bias at about 47°S (Figure 9).
We used the pattern correlation coefficient (PCC) to evaluate the subregional spatial agreement between CMIP6 models and the observation ensemble over the annual mean of precipitation and temperature across the reference period (1986-2014). Results are shown in Table 3 as the PCC average of precipitation and temperature for individual models. Overall, Northern Chile showed the largest PCC across all subregions with a median of 0.89 (IQR=0.06). Here, 30 out of 36 models (83%) scored PCC > 0.8, suggesting that CMIP6 simulations perform well in replicating the observed spatial pattern of precipitation and temperature. On the other hand, Southern Patagonia presented the lowest PCC of all subregions with a median of 0.65 (IQR=0.11). Only 1 out of 36 CMIP6 models in this subregion scored a PCC > 0.8 (Table 3). This result was triggered by a wet bias of more than 6 mm/day near coastal areas (Figure 8). Northern Patagonia showed a median score of 0.81 (0.04), and 18 out of 36 models (50%) had a PCC > 0.8. Central Chile’s median PCC was 0.78 (0.16), and 13 out of 36 models (36%) scored a PCC > 0.8. Since Central Chile’s precipitation bias was low, this score is attributable to the unsatisfactory simulation of the annual spatial mean of temperature in the northern extreme of this subregion (Figure 9-c). For entire Chile, 35 out of 36 models showed PCC > 0.8, and the best-performing models on PCC scores were: GFDL-CM4, GFDL-ESM4, EC-Earth3-Veg-LR, FGOALS-f3-L, and EC-Earth3-CC. Grouped by model family, GFDL and EC-EARTH3 best described the spatial pattern of precipitation and temperature. These results compare to the study of Rivera and Arnould (2020), who evaluated precipitation projections of 14 CMIP6 models for a region covering Central Chile and Northern Patagonia in our research. Though the study of Rivera and Arnauld (2020) is not directly comparable with our study (they analyzed projections of precipitation only over the longest period, 1901-2014), they applied a similar methodology to evaluate the spatial pattern of CMIP6 projections. As in our study, Rivera and Arnauld (2020) found that most models scored a PCC > 0.8 with a marked overestimation of precipitation.
Table 3. PCC ranking summary of CMIP6 models for Chile. Values show the regional average of PCC for the annual mean of precipitation and temperature compared against the ensemble of observations from 1986 to 2014. Bold values show the five best-performed models for Northern Chile (N. CHL), Central Chile (C. CHL), Northern Patagonia (N. Pat), Southern Patagonia (S. Pat), and the entire domain (Chile).
Model
|
N. CHL
|
C. CHL
|
N. Pat
|
S. Pat
|
Chile
|
ACCESS-CM2*
|
0.840
|
0.679
|
0.819
|
0.631
|
0.865
|
ACCESS-ESM1-5
|
0.800
|
0.660
|
0.838
|
0.739
|
0.890
|
AWI-CM-1-1-MR
|
0.839
|
0.883
|
0.810
|
0.646
|
0.881
|
BCC-CSM2-MR
|
0.846
|
0.734
|
0.790
|
0.659
|
0.821
|
CAMS-CSM1-0
|
0.873
|
0.844
|
0.796
|
0.599
|
0.867
|
CAS-ESM2-0
|
0.895
|
0.747
|
0.567
|
0.444
|
0.797
|
CESM2-WACCM*
|
0.912
|
0.765
|
0.776
|
0.746
|
0.861
|
CIESM*
|
0.920
|
0.789
|
0.802
|
0.721
|
0.889
|
CMCC-CM2-SR5
|
0.902
|
0.653
|
0.810
|
0.713
|
0.842
|
CMCC-ESM2
|
0.904
|
0.697
|
0.820
|
0.701
|
0.853
|
CanESM5*
|
0.774
|
0.742
|
0.720
|
0.547
|
0.826
|
E3SM-1-1*
|
0.894
|
0.831
|
0.774
|
0.669
|
0.892
|
EC-Earth3-AerChem
|
0.896
|
0.877
|
0.842
|
0.602
|
0.892
|
EC-Earth3-CC
|
0.894
|
0.866
|
0.843
|
0.618
|
0.893
|
EC-Earth3-Veg-LR
|
0.891
|
0.852
|
0.847
|
0.633
|
0.902
|
EC-Earth3-Veg
|
0.894
|
0.871
|
0.841
|
0.616
|
0.893
|
EC-Earth3
|
0.891
|
0.869
|
0.838
|
0.612
|
0.890
|
FGOALS-f3-L
|
0.917
|
0.856
|
0.757
|
0.556
|
0.895
|
FGOALS-g3
|
0.891
|
0.800
|
0.780
|
0.668
|
0.846
|
FIO-ESM-2-0
|
0.914
|
0.769
|
0.817
|
0.743
|
0.877
|
GFDL-CM4
|
0.947
|
0.905
|
0.829
|
0.649
|
0.923
|
GFDL-ESM4
|
0.943
|
0.903
|
0.800
|
0.647
|
0.917
|
IITM-ESM
|
0.849
|
0.781
|
0.793
|
0.554
|
0.888
|
INM-CM4-8
|
0.785
|
0.582
|
0.767
|
0.746
|
0.863
|
INM-CM5-0
|
0.787
|
0.600
|
0.786
|
0.734
|
0.866
|
IPSL-CM5A2-INCA
|
0.848
|
0.469
|
0.508
|
0.634
|
0.841
|
IPSL-CM6A-LR*
|
0.878
|
0.781
|
0.740
|
0.601
|
0.873
|
KACE-1-0-G
|
0.783
|
0.682
|
0.733
|
0.595
|
0.846
|
MIROC6
|
0.857
|
0.419
|
0.827
|
0.722
|
0.850
|
MPI-ESM1-2-HR
|
0.875
|
0.876
|
0.824
|
0.609
|
0.893
|
MPI-ESM1-2-LR
|
0.843
|
0.726
|
0.817
|
0.631
|
0.882
|
MRI-ESM2-0
|
0.938
|
0.839
|
0.805
|
0.604
|
0.890
|
NESM3*
|
0.856
|
0.701
|
0.810
|
0.664
|
0.868
|
NorESM2-LM
|
0.718
|
0.587
|
0.744
|
0.806
|
0.815
|
NorESM2-MM
|
0.881
|
0.787
|
0.819
|
0.745
|
0.881
|
TaiESM1
|
0.911
|
0.780
|
0.808
|
0.689
|
0.870
|
*Models with ECS values above the IPCC AR5 likely range (1.5°C to 4.5°C). ‘Hot models’ were identified from Tokarska et al (2020) and Scafetta (2022).
3.3 Model Ranking
So far, we have thoroughly evaluated the spatiotemporal performance of CMIP6 models across Chile. We finally summarized models’ performance by applying the Taylor Skill Score metric (TSS) over the annual cycle, which helped us to select the best-performing models for precipitation and temperature. TSS results are displayed in Table 4, with the five best-performing models shown in bold for each of four subregions and for entire Chile. Although models replicated the precipitation pattern in Northern Chile, they all strongly overestimated this variable during the rainy season (December, January, and February). This bias is represented on the TSS metric, which, averaged across models, was lowest among all subregions, including entire Chile (0.63). Those models with the best performance in describing the average annual precipitation cycle and temperature were: CAS-ESM2-0, FGOALS-f3-L, ACCESS-CM2, KACE-1-0-G, and MPI-ESM1-2-HR. The model ACCESS-CM2 is identified as a ‘hot model’ by Tokarska et al (2020) and Scafetta (2022). The bias of these warm models is discussed in section 3.4.
Central Chile scored the highest average TSS across all subregions (0.90). The best-performing models were: CanESM5, INM-CM4-8, CAMS-CSM1-0, IPSL-CM5A2-INCA, and MPI-ESM1-2-HR. It is worth noting that CanESM5 had the highest score among all CMIP6 models for Central Chile. This model was also identified as the best performing in the study of Rivera and Arnould (2020). However, it is a “hot model” and, as it will be shown in section 3.4, presents the warmest temperature projection for the end of the century. Therefore, care should be taken when considering this model for future temperature predictions in Central Chile. Another interesting feature of Central Chile is that the model ensemble scored the sixth-best TSS score for the subregion (0.95).
Northern Patagonia scored Chile's second-highest average TSS metric (0.87). The best-performing models were: GFDL-CM4, FGOALS-f3-L, EC-Earth3-CC, EC-Earth3, and IPSL-CM5A2-INCA. In Southern Patagonia, the ensemble mean had the highest TSS (0.84), and the best-performing models were: FGOALS-f3-L, MIROC6, NorESM2-LM, FGOALS-g3, and MPI-ESM1-2-HR. The best-performing models for Chile were: AWI-CM-1-1-MR, NorESM2-LM, MPI-ESM1-2-HR, IPSL-CM5A2-INCA, and IITM-ESM.
Table 4. TSS ranking summary of CMIP6 models for Chile. Values show the regional average of TSS for the precipitation and temperature annual cycles compared against the ensemble of observations from 1986 to 2014. Bold values show the five best-performed models for Northern Chile (N. CHL), Central Chile (C. CHL), Northern Patagonia (N. Pat), Southern Patagonia (S. Pat), and the entire domain (Chile).
Model
|
N. CHL
|
C. CHL
|
N. Pat
|
S. Pat
|
Chile
|
ACCESS-CM2*
|
0.811
|
0.865
|
0.833
|
0.771
|
0.816
|
ACCESS-ESM1-5
|
0.571
|
0.885
|
0.775
|
0.715
|
0.761
|
AWI-CM-1-1-MR
|
0.762
|
0.937
|
0.904
|
0.767
|
0.919
|
BCC-CSM2-MR
|
0.551
|
0.876
|
0.862
|
0.411
|
0.603
|
CAMS-CSM1-0
|
0.571
|
0.967
|
0.825
|
0.533
|
0.842
|
CAS-ESM2-0
|
0.864
|
0.837
|
0.88
|
0.778
|
0.809
|
CESM2-WACCM*
|
0.615
|
0.847
|
0.811
|
0.568
|
0.863
|
CIESM*
|
0.618
|
0.842
|
0.788
|
0.661
|
0.756
|
CMCC-CM2-SR5
|
0.53
|
0.826
|
0.894
|
0.639
|
0.642
|
CMCC-ESM2
|
0.521
|
0.871
|
0.869
|
0.573
|
0.625
|
CanESM5*
|
0.56
|
0.979
|
0.915
|
0.71
|
0.648
|
E3SM-1-1*
|
0.559
|
0.956
|
0.893
|
0.692
|
0.702
|
EC-Earth3
|
0.652
|
0.925
|
0.922
|
0.663
|
0.81
|
EC-Earth3-AerChem
|
0.669
|
0.923
|
0.901
|
0.726
|
0.833
|
EC-Earth3-CC
|
0.633
|
0.921
|
0.923
|
0.684
|
0.844
|
EC-Earth3-Veg
|
0.646
|
0.924
|
0.896
|
0.702
|
0.85
|
EC-Earth3-Veg-LR
|
0.625
|
0.891
|
0.867
|
0.642
|
0.82
|
FGOALS-f3-L
|
0.855
|
0.836
|
0.935
|
0.832
|
0.866
|
FGOALS-g3
|
0.515
|
0.935
|
0.921
|
0.792
|
0.607
|
FIO-ESM-2-0
|
0.521
|
0.88
|
0.833
|
0.774
|
0.753
|
GFDL-CM4
|
0.625
|
0.948
|
0.939
|
0.748
|
0.823
|
GFDL-ESM4
|
0.605
|
0.91
|
0.91
|
0.76
|
0.831
|
IITM-ESM
|
0.582
|
0.779
|
0.896
|
0.684
|
0.872
|
INM-CM4-8
|
0.604
|
0.975
|
0.811
|
0.616
|
0.831
|
INM-CM5-0
|
0.572
|
0.91
|
0.813
|
0.61
|
0.779
|
IPSL-CM5A2-INCA
|
0.72
|
0.956
|
0.921
|
0.708
|
0.884
|
IPSL-CM6A-LR*
|
0.658
|
0.836
|
0.909
|
0.652
|
0.794
|
KACE-1-0-G
|
0.807
|
0.891
|
0.864
|
0.673
|
0.832
|
MIROC6
|
0.584
|
0.817
|
0.782
|
0.822
|
0.803
|
MPI-ESM1-2-HR
|
0.798
|
0.952
|
0.879
|
0.779
|
0.89
|
MPI-ESM1-2-LR
|
0.575
|
0.936
|
0.819
|
0.752
|
0.811
|
MRI-ESM2-0
|
0.564
|
0.782
|
0.867
|
0.634
|
0.756
|
NESM3*
|
0.543
|
0.922
|
0.74
|
0.459
|
0.777
|
NorESM2-LM
|
0.603
|
0.912
|
0.882
|
0.808
|
0.918
|
NorESM2-MM
|
0.627
|
0.95
|
0.869
|
0.701
|
0.856
|
TaiESM1
|
0.541
|
0.872
|
0.821
|
0.684
|
0.797
|
Ensemble
|
0.611
|
0.95
|
0.902
|
0.84
|
0.867
|
*Models with ECS values above the IPCC AR5 likely range (1.5°C to 4.5°C). ‘Hot models’ were identified from Tokarska et al (2020) and Scafetta (2022).
3.4 Future Projections (2080-2099)
In this section we analyze the changes in temperature and precipitation at the end of the century with respect to historical period under four emission scenarios (Figure 11 and 12, respectively). The precipitation changes are spatially consistent in Central Chile and Northern Patagonia and become robust (at least 90% of models agree on the sign of precipitation change) in the scenario SSP245, which shows a precipitation reduction by 10-20% (Figure 11). This reduction is much more substantial and spatially robust when increasing the strength of the anthropogenic radiative forcing. In Central Chile, under scenario SSP585, CMIP6 models project a mean reduction of 30-40% of annual precipitation that is spatially consistent across 90% of CMIP6 GCMs. Similarly to Almazroui et al. (2021), we found that the changes tend to become stronger with increasing radiative forcing, suggesting a potentially simple proportional scaling. Less inter-model agreement results for projected precipitation change in the extreme dry and wet subregions. In Northern Chile, the CMIP6 ensemble forecasts up to 20% decrease in mean annual precipitation. However, this tendency is inconsistent across GCMs (non-robust change) as well as across the scenarios as a decrease is projected under scenarios SSP126, SSP245 and SSP370, and a general non-robust increase in precipitation under scenario SSP585 (Figure 11). The robust drying projected for the Central Chile and Northern Patagonia extends to the northern part of Southern Patagonia in scenarios SSP370 and SSP585, reaching up to 20% at around parallel 47°S. In the southernmost portion of the latter subregion the sign of the changes varies amongst GCMs (see Figure 13) showing a non-robust increase in the ensemble mean.
Our findings are in agreement with previous studies focused on the future climate change in Chile in CMIP5 and CMIP6. For instance, Bozkurt et al. (2018) reported a drying of up to ~30% over Central Chile using projections from CMIP5 by the end of the century. The trends identified in CMIP5 are consistent with recent CMIP6 models that project a robust drying over Mediterranean-type climate regions, including Central Chile (Cook et al., 2020). When compared with the historical period, future changes in precipitation over Central Chile are more significant than the baseline variability under scenarios SSP370 and SSP585, and changes in precipitation become temporarily and spatially robust from mid-century onwards, reaching -2mm/day compared to the historical period (Almazroui et al., 2021). CMIP6 projected changes in precipitation, especially in Central Chile and Northern Patagonia, are related to a change in the width and strength of the Hadley cell with a poleward storm-track shift. This implies a southern expansion of the band of subtropical subsidence, leading to enhanced mid-latitude tropospheric warming and poleward shifts of the subtropical dry zone and increased subtropical drought events documented since 1979 (Hu et al., 2011; Huang et al., 2016). This change in general circulation features is replicated by CMIP6 models, which show a total annual-mean trend in the width of the Hadley cells of 0.13° ± 0.02° per decade over 1970–2014 across historical simulations (Xia et al., 2020) and that is 2–3 times larger in the Southern Hemisphere (SH) (Grise and Davis, 2020). It’s been suggested that natural SST variability primarily related to El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) are the main factors explaining the observed shift patterns (Allen and Kovilakam, 2017). PDO also contributes about half of the observed precipitation trend in Central Chile (Boisier et al., 2016), which is expected to be reinforced in the future by anthropogenic forcing. As consequence, Central Chile will experience the strongest increments in meteorological droughts by the end of the century with high CMIP6 intermodel agreement (Ukkola et al., 2020). The intensity of these drought events is much stronger and more robust in CMIP6 compared to CMIP5. This trend also extends to Northern Patagonia, which is projected to be affected by an increase in the duration, hydrological deficit, and frequency of severe droughts (Aguayo et al., 2021; Garreaud, 2018).
Figure 12 shows the temperature projections over Chile from the CMIP6 ensemble. In the case of temperature, we evaluated the robustness of changes in sign and magnitude by identifying those grid cells where the projected ensemble mean change is at least twice the standard deviation of the reference period (Almazroui et al., 2021; Scheff and Frierson, 2012), which is much stronger criterion that in the case of precipitation for which the robustness is estimated based on the sign agreement among the models. In contrast to precipitation, temperature changes incrementally in all models, all subregions and all emission scenarios. CMIP6 projects a mean annual temperature increase between 0-2°C in SSP126, 1-3°C in SSP245, 1-5°C in SSP370, and 2-6°C in SSP585 by the end of the century. Amongst all subregions, Northern Chile displays the greatest increments in temperature ranging from 1-2°C in the lowest emission scenario to 4-6° in the highest emission scenario, with the maximum change occurring in high mountains. Robust changes in temperature are present in only a few grid points in SSP370 and over the Andes range in SSP585. The second warmer projection occurs in Central Chile from about 1-1.5°C in SSP126 to 4-5°C in SSP585. The strongest increments in mean annual temperature are also presented across the Andes range with a 4-5°C greater temperature than the reference period. The magnitude of these changes is robust only in the southern portion of the subregion and alongside the coast in the intermediate emission scenario SSP370 and more explicit in the high emission scenario SSP585. This pattern is also present in Northern Patagonia, where robust changes in temperature become visible in its meridional extreme in emission scenario SSP370 and widespread in the high emission scenario SSP585. In this scenario, CMIP6 projects a robust increase up to 2-3°C across all the subregions, with the most significant changes alongside the Andes range. Temperature changes in Southern Patagonia become robust in scenario SSP370 with increments of 2-3°C and 3-4°C change over the Andes in the high emission scenario SSP585 (Figure 12). The projected increase in Andean temperature across subregions might be related to the known elevation-dependent warming (EDW), where high-mountain environments experience more rapid temperature changes than environments at lower elevations (Pepin et al., 2015). Recent evidence of EDW in the Andes of Northern and Central Chile has been reported using observation and modeling approaches (Aguilar-Lome et al., 2019; Bambach et al., 2022). Though effective EDW is challenging to validate because sparse high-elevation weather stations and high cloud cover hinder satellite analysis (Pabón-Caicedo et al., 2020), its consequences may significantly impact cryospheric systems, hydrological regimes, ecosystems, settlements, and productive systems.
We finally investigated the relationship between projected precipitation and temperature change for the end of the century in the high-emission scenario SSP585. This is plotted on a two-dimensional space in Figure 13 and reveals the general pattern of projected changes and the behavior of individual GCMs across different subregions. The trend for sub-regionally averaged change in precipitation and temperature is evident in Central Chile, where GCMs are clustered towards the axis of negative precipitation and positive temperature change ranging from 2.2% to -37.6% and 2.2°C to 5.3°C, respectively. A clear pattern of change is also visible in Northern Patagonia, where all models project a negative shift in precipitation from -6.6% to -31.5% and a positive temperature change from 1.69°C to 4.42°C. For the remaining subregions, the pattern of precipitation change is not conclusive. However, all project positive changes in temperature from 2.85°C to 6.15°C in Northern Chile and from 1.43°C to 4.19°C in Southern Patagonia (Figure 13).
Interestingly, the GCM that shows the greatest increment in temperature for Central Chile is CanESM5 (5.3°C), which ranked as the model with the highest TSS value (Table 4). Recently, Rivera and Arnould (2020) reported the same model as the best performing in describing the current precipitation pattern for Central Chile. However, given that CanESM5 is identified as a ‘hot model’ (Scafetta 2022) its projections must be taken carefully unless model weighting or rescaling the ensemble is applied to avoid highly biased projections (Tokarska et al., 2020). Also, choosing the ensemble with the more reliable models has been proposed (Scafetta 2021). Similarly, the ‘hot model’ ACCESS-CM2 was the best performing model for Northern Chile, and therefore the same care must be applied in using its raw projections.