Precipitation and temperature zoning of Khorasan Razavi province using data from the sixth climate change report (CMIP6)

The ongoing climate change will change all aspects of biological systems, from genetics to ecosystems (Scheffers et al. 2016, 724). By 2100, climate change will make extinct one sixth of animal and plant species and will change the abundance and distribution of many remaining species, which will result in new communities (Urban, 2015, 573). Based on this, it will be very important to investigate the annual and decade changes of average temperature and precipitation at the regional level. It is also important to understand how temperature changes and related indicators such as heat stress in different time scales in order to make informed decisions regarding economic development and climate action plans (CAP).
One of the main sources of data for the study of climate change are general circulation models (GCM), which are widely used to monitor and predict past and future climate change (Khan et al. 2020). GCMs have a remarkable ability to simulate temperature and precipitation. However, they also have limitations. Among these limitations are systematic errors in reproducing average temperature and precipitation, especially in areas with complex topography, such as Iran (IPCC, 2013). In this context, IPCC, as the most important reference for researches and forecasts related to climate change, has so far presented several generations of emission surveys and based on the results of different climate change modeling, six climate change assessment reports. has published. In the recent IPCC report, the latest climate change models are called CMIP6 series, which simulate the future climate under ssp emission scenarios (IPCC, 2021). The final version of the CMIP6 model design was confirmed by the two CMIP working groups and the WGCM coupled model work group in October 2014, and the complete results and various models are expected to be published before the end of 2022. The scenarios of the sixth report are a combination of socio-economic trajectories (ssp) (sustainable development sp1, development based on intermediate policies sp2, regional competition sp3, inequality sp4, and fossil fuel development sp5) and greenhouse gas concentration trajectory different levels of coercion) are produced; So that they provide the possibility of feedback analysis between changes and socio-economic factors such as global population growth, economic development and technological progress.

In this report, eight scenarios are presented in two rows. The first row includes the new scenarios SSP1-2.6, SSP2-4.5 and SSP5-8.5, which are respectively the updated scenarios of the forcing levels RCP2.6, RCP4.5 and RCP8.5 of the fifth report. The SSP1-2.6 scenario shows the lowest level of radiative emissions; SSP2-4.5 considers the average forcing level and SSP5-8.5 presents the upper limit of radiative forcing (Stock et al. 2020, Rogelj et al. 2018). In addition to these three scenarios, the non-decreasing forcing scenario (SSP3-7.0) with high emission of suspended particles and land use change has been added in this group. In the second row, two mitigation scenarios have been added to achieve a relatively low forcing output and one scenario considering limiting the average global temperature to below 1.5 degrees Celsius compared to forcing levels over industrialization and a scenario with a very high trajectory.
GCM models are the best tools for investigating the effects of climate change on weather parameters. These models are three-dimensional and are able to model and produce atmospheric and oceanic parameters for a long-term period on a global or continental scale, taking into account the approved IPCC scenarios (Chen et al., 2019). Considering the importance of uncertainty analysis, evaluation and selection of GCMs based on their performance in simulating climate variables is a method that can be used to select the best models and reduce uncertainties (Zamani et al. , 2020). The importance of water in Razavi Khorasan Province led the current research to reveal the trend of spatial and temporal changes of precipitation and temperature in the region in current conditions and also to evaluate the performance of CMIP6 models in reproducing annual and seasonal precipitation under the combined scenarios of common socio-economic paths (SSPS). ) to be concentrated in the future climate. Forecasting climate change plays an essential role in improving the understanding of the climate system and also identifying its social risks in the future. cmip6 activities focused on scenarios were formed in 2013 among climate communities. The Scientific Steering Committee of ScenarioMIP for this project was formed from the 17th meeting of the World Climate Research Working Group (WCRP) in October 2013 in Victoria, Canada. The main activity in Phase 6 of the Coupled Model Cross Project (CMIP6) is the Scenario Cross Project (ScenarioMIP). that the prediction of these climate models is a combination of a new set of release and land use scenarios produced by IAMs models based on the common socio-economic trajectories (SSP) of the future (which includes elements such as population, economic growth, urbanization, age, education and…) and is related to RCPs greenhouse gas concentration scenarios. This new structure provides two important elements in the designed space of scenarios, first: it standardizes all the socio-economic assumptions in each scenario, and secondly, it allows for a more detailed examination of various trajectories that can be It achieves the future climate results. The climate forecast of the CMIP6 project is different from the CMIP5 projects due to the production of updated versions of climate models and the use of SSP-based scenarios based on the updated data in the process of publication. The scenarios of joint socio-economic trajectories (SSPs) are the new group of scenarios of non-climate emissions resulting from coupled models of the sixth phase of climate change (CMIP6) in line with the sixth assessment report of climate change (AR6). These scenarios are presented with the aim of providing forecasts in the common socio-economic path. These scenarios include possible alternative changes in social aspects such as demographic, economic, technological, social, governance and environmental factors based on integrated analyzes of climate impacts, vulnerability, policies related to adaptation and They describe adjustment.
To determine the accuracy of each CMIP6 model, the simulation results of rainfall and temperature of each basin in the historical period were compared with observational statistics. In this step, the Kling-Gupta statistical test (KGE) was used to determine the accuracy of each model (correlations). This measure, while being simple, includes the mean, standard deviation, and correlation coefficient of the series of observation and simulation data obtained from the model, and weighting based on this measure can be of great help in increasing the accuracy of the modeling results. And finally, for micro-scale, Oribi method based on various approaches such as probability distribution mapping, empirical cumulative distribution function mapping, quantile mapping and quadrature density distribution mapping is presented, which is used in many studies to evaluate networked and recorded rainfalls. to be This method works by correcting the mean, standard deviation and quantiles by equalizing the distribution functions of model outputs and observational data. In the bias method, it is assumed that the simulated and observed precipitation follow the same proposed distribution, which in turn may cause bias. Based on this, gamma distribution in the form of α and β scale is often used for the distribution of precipitation events.

The output of general atmospheric circulation models in terms of temporal and spatial resolution is about tens of kilometers on a daily and monthly scale, which are large scale compared to climate processes. In addition, GCM simulations in both temporal and spatial scales have uncertainty in the parameterization of processes, so the output of these models cannot be directly used in climate change studies. Therefore, exponential scaling and skew correction of GCM simulations are necessary to obtain information at a suitable scale. Therefore, the present study investigated and evaluated the accuracy of CMIP6 models, which were recently published by IPCC, for the simulation of temperature and precipitation in Razavi Khorasan Province. For this purpose, the output of GCM models during the period of 1988-2018 was extracted and their accuracy was based on the observational data of seven synoptim stations (Mashhad, Gonabad, Qochan, Sarkhes, Sabzevar, Tarbiat Heydarieh and Torbat Jam) using the index King-Kupta (KGE) was investigated. The results showed that the GESM2 model has the highest accuracy for temperature estimation and the HadGEM3-GC model for the precipitation variable, and also based on this person, it was determined that in CMIP6 models, temperature is more accurate than precipitation. The results of rainfall variable simulation by CMIP6 climate models under three scenarios SSP1-2.6, SSP2-4.5 and SSP5-8.5 during the upcoming period (2020 to 2100) for Razavi Khorasan province showed that in all stations of this province We will witness an increase in rainfall from 0.40% to 6.8%, the most increase in rainfall will occur in the east of the province and the least increase in rainfall will be in the center of the province. The average temperature in all parts of the province under the CMIP6 scenarios will be from 0.29 degrees Celsius to 2.75 degrees Celsius, and the largest increase will be related to Mashhad and Sabzevar stations.