Uncertainty Assessment of AOGCMs and Emission Scenarios in Climatic Parameters Estimation (Case Study in Mashhad Synoptic Station)

Document Type : Research Article

Authors

• Delaram Hooshmand
• Mohammad Javad Khordadi

Ferdowsi University of Mashhad

10.22067/geo.v3i3.29927

Abstract

. Introduction
Global warming and its result, climate change is an important subject investigated especially in the recent decades by researchers throughout the world. In these studies, at first, climatic parameters changes are investigated. Considering many uncertainties in the parameters estimation, it is better to choose a method in order to study and analyze the uncertainty band due to different sources of uncertainty. To investigate climate change impact on different sources in the future periods, at first climatic parameters should be simulated under climate change effect. Various methods simulate the parameters, but the most valid method is application of AOGCM (Atmosphere-Ocean General Circulation Model). A general circulation model is a three-dimension mathematical model of the general circulation of a planetary atmosphere or ocean and based on the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources (radiation, latent heat). These equations are the basis for complex computer programs commonly used for simulating the atmosphere or ocean of the Earth. Atmospheric and oceanic GCMs (AGCM and OGCM) are key components of global climate models along with sea ice and land-surface components. AOGCMs are widely applied for weather forecasting, understanding the climate, and projecting climate change. One of the most important uncertainty sources in climate change field is related to AOGCMs and emission scenarios which makes different outputs for climatic variables.
Atmospheric carbon dioxide levels have been recorded continual increases since the 1950s, a phenomenon that may significantly alter the global and local climate characteristics, such as temperature and available water resources. IPCC (Intergovernmental Panel on Climate Change) predicted that the world mean temperature will be increased up to 1°C by the year 2050 and up to 3°C by the end of the next century.
Estimates of global warming are generally based on the application of general circulation models (GCMs), which attempt to predict the impact of increased atmospheric CO2 concentrations on the weather variables. Owing to the complex mechanism in the atmosphere motion and the uncertainty of the model structure, different GCMs produce different predictions.
2. Study Area
In this study, Mashhad synoptic station is chosen as the study area. The station is located in north east of Iran with 59 degrees 38 minutes of eastern longitude and 36 degrees 16 minutes of northern latitude. Its elevation is 999 meters from sea level. The situation of the region is showed in Figure 1.
3. Material and Methods
In this paper, investigating the uncertainty band due to fifteen AOGCMs (including BCM2, CGMR, CNCM3, CSMK3, FGOALS, GFCM21, GIAOM, HADCM3, HADGEM, INCM3, IPCM4, MIHR, MPEH5, NCCCSM and NCPCM) under three emission scenarios including A1B, A2 and B1, changes of minimum temperature, maximum temperature, and rainfall parameters in Mashhad synoptic station located in Ghareghom basin are studied. Also LARS-WG model is used for downscaling. Studying outputs of different models, it is tried that emission scenarios analysis for each parameter is done and uncertainty band due to fifteen models for emission scenarios is drawn. These band show possible changes for the studied parameters in the future period to the past one.
AOGCMs are the most suitable tools to study climate change phenomenon, but regarding large-scale spatial resolution of these models, regional scale is not possible. In other words, the models simulate climatic parameters in large scale whereas comparing the output for historical periods with observed data shows difference. Therefore, various methods of downscaling are created.
LARS-WG is a stochastic weather generator which can be used for the simulation of weather data at a single site under both current and future climate conditions. These data are in the form of daily time-series for a suite of climate variables, namely, precipitation (mm), maximum and minimum temperature (°C) and solar radiation (MJm-2day-1). Stochastic weather generators were originally developed for two main purposes: Firstly, providing a means of simulating synthetic weather time-series with statistical characteristics corresponding to the observed statistics at a site, but which were long enough to be used in an assessment of risk in hydrological or agricultural applications. Secondly, to provide a means of extending the simulation of weather time-series to unobserved locations, through the interpolation of the weather generator parameters obtained from running the models at neighboring sites.
The most recent version of LARS-WG has undergone a complete redevelopment in order to produce a robust model capable of generating synthetic weather data for a wide range of climates. LARS-WG has been compared with another widely-used stochastic weather generator, which uses the Markov chain approach at a number of sites representing diverse climates and has been shown to perform at least as well as, if not better than, WGEN at each of these sites (Semenov et al, 1998).
4. Results and Discussion
To validate LARS-WG model, observation data and simulated one in the base period (1976-2005) for values of minimum temperature, maximum temperature and rainfall for each month are drawn in graphs. For each three parameters the model could have suitable accuracy that maximum differences are 0.6°c (May), 0.7° (November) and 0.14 mm (January), respectively. Maximum and minimum uncertainty band of minimum temperature parameter are for A1B (12.5°c in year) and B1 (11.7°c in year) scenarios, respectively. These values for maximum temperature parameter are for A1B (15.7°c) and A2 (11.3°c) scenarios, and finally for rainfall parameter are for A1B (3.5 mm in year) and B1 (3.1 mm in year). Increasing minimum temperature for the future period for A1B, A2 and B1 are between (0.5-1.4°c), (0.7-1.6 °c) and (0.7-1.7°c), respectively. The values for maximum temperature parameter in the same order are (0.3-1.6°c), (0.5-1.5°c) and (0.4-1.6°c). For rainfall all the scenarios have approximately -0.1 to +0.2 mm changing values.
5. Conclusion
LARS-WG is able to simulate climatic parameters in the base period, since the modeled parameters and observation ones have high closeness in all months. The uncertainty due to emission scenarios for different parameters is varied. The results indicate the highest uncertainty band is related to A1B scenario, but about the lowest one various conclusions are achieved. In other words, B1 scenario for minimum temperature and rainfall parameters and A2 scenario for maximum temperature parameter are introduced.
Finally, Estimation of long duration changes in climatic and hydrological variables concludes necessity of appropriate management of water resources considering impacts of climatic changes, especially for the regions having arid and semi-arid climate like the studied region.

Keywords

• Climate change
• Uncertainty
• Downscaling
• Minimum temperature
• Maximum temperature
• rainfall

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