Evaluating the effects of climate change on precipitation and temperature for Iran using RCP scenarios

Climate change has caused many changes in hydrologic processes and climatic conditions globally, while extreme events are likely to occur more frequently at a global scale with continued warming. Given the importance of general circulation models (GCMs) as an essential tool for climate studies at global/regional scales, together with the wide range of GCMs available, selecting appropriate models is of great importance. In this study, six synoptic weather stations were selected as representative of different climatic zones over Iran. Utilizing monthly data for 20 years (1981 – 2000), the outputs of 25 GCMs for surface air temperature (SAT) and precipitation were evaluated for the historical period. The root-mean-square error and skill score were chosen to evaluate the performance of GCMs in capturing observed seasonal climate. Finally, the outputs of selected GCMs for the three Representative Concentration Pathways emission scenarios (RCPs), namely RCP2.6, RCP4.5, and RCP8.5, were downscaled using the change factor method for each station for the period 2046 – 2065. Results indicate that SAT in all months is likely to increase for each region, while for precipitation, large uncertainties emerge, despite the selection of climate models that best capture the observed seasonal cycle. These results highlight the importance of selecting a representative ensemble of GCMs for assessing future hydro-climatic changes for Iran.


INTRODUCTION
outputs has suggested that drier areas will likely become drier and wetter areas will likely become wetter, with a notable expansion in arid and semi-arid climates (Feng et al. ; WWAP/UN-Water ). Nevertheless, a comprehensive investigation of regional and seasonal impacts of CC in the future is needed, particularly at regional scales (Aloysius et al. ). that wet regions will likely become wetter, and dry regions will likely become drier. However, the extent to which future CC will affect regional shifts is still uncertain and differs from region to region. Taye  the 'Karaj-Jajrud' located in the South Alborz range. In comparison with the baseline period, all scenarios showed a consistent growth in SAT and a reduction in precipitation, while precipitation-series uncertainty was found to be more than the air temperature series.
Although previous studies provide essential information about potential CC impacts (Zarghami et

GCMs and CC scenarios
There are many factors to consider when selecting a model   as temperature and precipitation. The main problem in using the output of GCMs is the low resolution of output grids which is too coarse to be useful for regional studies.  Table 2 shows the summary characteristics of the GCMs used in the current study.

Annual evaluation
The Taylor

Seasonal evaluation
Although the annual analysis illustrates a good overall picture of models' performance, the results can vary significantly in other temporal scales (e.g. seasonal).
Therefore, the seasonal performance of the precipitation and SAT for all selected GCMs was examined using the RMSE and skill score (SS), and the best models were selected based on their seasonal performance. RMSE is always non-negative, and a value of 0 would indicate a perfect fit to the data. The SS index is used to evaluate the goodness of fit of a model prediction. It ranges from À∞ to 1 with SS ¼ 1 reflecting the perfect match of simulated and observed data, and SS ¼ 0 shows that the model predictions are as accurate as of the mean of the observed data. The expansions of the model identifiers can be found in http://www.ametsoc.org/Pubsacronymlist.
RMSE and SS are defined as follows: where m, o, and ōare simulated, observed, and mean of the The change factor method can be applied using Equations (3) and (4)  was used as the historical reference period.

Precipitation
We used the Taylor diagram to evaluate the overall performance of GCMs for annual precipitation. Figure Figure S1 in the Supplementary section.
As illustrated in Figure 4, some models perform better than others; however, their overall performance is similar.
The performance of the CMIP5 models for the Babolsar station is weaker, but all other stations illustrate the corre-

Model selection
The ranking of models based on SS is presented in Table 3.
In this table, the relative values of RMSE are shown as the  color spectrum in which darker colors represent better performance (smaller RMSE). It can be seen that RMSE and SS rankings of individual models are quite similar.
Tables S1-S6 in the Supplementary section contain the value of the SS score for different GCMs as well as additional indices, which were calculated to support our findings.

Model selection
Similar to precipitation, we examined the ability of the GCMs to simulate the seasonal SAT. The same statistical indices, i.e. RMSE and SS, were used together to select GCMs with the best performance in the baseline period.
The selected models were utilized to project future SAT changes at each station (Table 5). It is noteworthy that similar to precipitation, additional indices were also calculated for the SAT but were not used in model selection and are presented in the Supplementary section (Tables S7-S12).    Uncertainty is an indispensable part of GCM predictions which can be derived from their natural variability and coarse resolutions (Hawkins & Sutton ; Ahmadalipour et al. ). For that reason, selecting models that appropriately represents the regional-scale climate is an essential step before performing a regional CC impact assessment (Ahmadalipour et al.   It is noteworthy to mention that the selection of models in the current study is dependent on which metrics/skill scores you assess against. It means that if you were to look at how GCMs capture extremes or modes of variability that affect a region, you would most likely get a different set of climate models. Also, the models were chosen according to their seasonal performance, and there is no guarantee that this will remain the best model for other time scales.
Therefore, there is a risk that uncertainties in future projections under-representing and should be considered in future studies. The question that arises here is that if the selected models for different seasons affect the physical integrity of the simulations. These models might be right for the wrong reasons or vice versa. This makes the future simulations lack internal consistency, which is suggested to be analyzed more in future works.

CONCLUSION
In this study, utilizing monthly data for 20 years (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000), the outputs of 25 GCMs for SAT and precipitation were evaluated using observations for six synoptic stations over Iran. The performance of these models was evaluated using RMSE and SS, and the best models were selected at seasonal time scale and accordingly, the future data were generated. Although the annual performance of models was different, most models show an acceptable representation of the annual cycle of each variable at most stations. The GCM predictions at different time scales showed dissimilar uncertainties. Evaluation of the results of the present study illustrates that models generally perform better in simulating SAT compared to precipitation. The majority of the models was unable to simulate the temporal pattern of precipitation at seasonal scales at all stations.
Therefore, there is less confidence in precipitation projections in comparison with SAT projections due to the unpredictable nature of the former. The results showed that the GCMs tend to over/underestimate climatic variables on regional and global scales, failing to resolve the microscale climate. Also, the results revealed that models with finer resolution do not always perform better than those with coarser resolutions. In most stations, some models provide realistic figures for some seasons in the baseline period. However, they failed to provide reasonable outputs for all seasons over the years. Overall, the results showed that the mean SAT is expected to increase in all seasons, while the precipitation change did not follow a specific trend. The outcomes of this study and related research can be a stimulus for the government to find new and sustainable adaptation strategies for the water sector.