A.2 Time scale database of climate parameters

Project manager: Gábor Illés (FRI)

 

Research plan

 

Analysis of field:

 

Climate change is one of the most important and influential ecological problem of our time (Harnos et al, 2008; Szép, 2010). Both the global and the regional scale climate models have gone through significant improvement in the past periods. These climate models are used primarily in the modelling of temperature regimes of the present and to some extent in the modelling of the amount of precipitation. Their common feature is that their reliability is passable in case of large spatial scales (Randall el al., 2007). In the case of large spatial scales the values and trends simulated by the models are correlated well with the observed values (Hegerl et al., 2007). However, the uncertainty of the anticipated temperature change is considerable, about 50% (Knutti et al., 2008), which is caused primarily by the doubtfulness of the modelling of the carbon cycle (Friedlingstein et al., 2006). The applied models indicate change in the number of extreme precipitation events (Tebaldi et al., 2006), but the uncertainty of the modelling grows apace when instead of continents smaller regions are forecasted. The changeableness of the climate and the lack of climate stability that manifests itself in long time intervals are significant from the point of view of the state of many agricultural areas. The scale of changeability can show significant heterogeneity both in space and time. The scale of this changeability and its spatial and temporal markings are, on the other hand, considerably scale dependent attributes in both respects (Dede et al, 2009). Because of these it is very important to have the most elaborate climactic databases for evaluating the effects of climate change on agriculture when it is unavoidable to deal with changes endured by small geographical areas and farming units. The source of these data can be ground-based professional observation systems (OMSZ) and systems based on remote sensing technologies (MODIS). Spatially explicit data files can be obtained from both systems. From the ground-based observation systems with interpolation methods and in the case of remote sensing instantly from the method of data collection spatially continuous data files arise. These data files can be incorporated into a unified framework with modern GIS systems after they become evaluable and comparable. The third source of data is the data files of scaled climate models (WDCC).

 

Justification, the problem supporting the necessity of research:

 

Derived from the abovementioned points, it is important to have an information technological background that incorporates data that characterizes the climate from various sources, and which helps the establishment of a decision support system that deals with the determination of adaptation strategies beside and beyond the subprograms of the project.

 

Aim of research:

 

The aim of the work is a climactic data system that builds from various data sources and works in a geographical informational system, and which can serve as a basis for the tasks of the connected scope of activities. We make it possible to characterize the reliability of measured temperature maps and the spatial variability of temperature data in a spatially explicit way. The directions of the past changes of temperature values and their scales will be evaluable, which can be compared with the forecasts of the climate models. 

 

Introduction of activity:

 

First, we compile the past (2000-2011) remote sensed data (temperature) and the entire time series of the meteorological base period (1970-2000). We aggregate the data monthly, seasonally and annually to form a temporal marking which is appropriate for the temporal resolution of meteorological data and is comparable with them. Within the scope of the task the raster files produced and/or downloaded from the data collected with remote sensing and ground-based observations have to be integrated into a geographical information system (fitting into projection). The raster data files have to be formed to a lattice distribution appropriate for the remote sensed data resolution (resampling). The validation of the fact and model data related to climate change is an important task, which ensures the equivalence of the applied climate models and the meteorological data with the remote sensed data. After compiling the data files we carry out analyses with which we explore the spatial variability of the remote senses data, which will be comparable with the spatial variability markings of the official temperature maps and climate models of the related periods. We examine the change of pixel values of the same pixels of the remote sensed data in periods from observation to observation and in the whole measurement duration.

 

Timing:

 

The task will be executed in the first half-year of the project.

1st month: establishment of the information technological background.

2nd-3rd months: data collection.

4th-5th months: data processing.

6th month: compilation of a coherent database.

 

Data to be used, partners:

 

M = 1 : 100 000 OMSZ digital monthly precipitation sum and average temperature models with 500x500 m cell size. These data files are coverages made on the basis of the long-term average of the years in the base period between 1970 and 2000. We will use the discrepancies from the present state estimated by the climate change scenarios for these.

 

The MODIS/Terra surface temperature and emission (LST/E) raster databases for the period from 2000 to 2011, which give temperature and emission values on the basis of pixels on grid-base on a global scale. The spatial resolution of raster files is 1000 meters, their time scale is 1 day.