Istvan Matyasovszky

Estimation of local precipitation statistics reflecting climate change

A stochastic approach is developed to estimate the probability of wet days, and the mean and standard deviation of local daily nonzero precipitation reflecting global climate change scenarios. The approach is based on the analysis of daily atmospheric circulation patterns (CPs) and the linkage between types of CPs and daily precipitation. Three CP data sets are used for the 500-hPa pressure field: 40-year historical, 10-year 1×CO2, and 10-year 2×CO2 scenarios obtained from the atmospheric general circulation model of the Canadian Climate Centre. CP types obtained by clustering techniques and their frequency distribution are similar for the three data sets. The linkage between CP types and precipitation considers an additional variable, the spatial average pressure height within each CP type. The difference in pressure heights among the three CP data sets makes it possible to estimate the effect of global change on local precipitation statistics. Under the dry continental climate of eastern Nebraska the effect of 2×CO2 scenario on local precipitation regime is spatially variable and significant: the number of wet days slightly decreases but both the mean and variance of daily precipitation increase resulting in a more variable precipitation regime.

Space-time precipitation reflecting climate change

Abstract A methodology has been developed and applied to an eastern Nebraska, USA, case study to estimate the space-time distribution of daily precipitation under climate change. The approach is based on the analysis both of the type and of the Markov properties of atmospheric circulation patterns (CPs), and a stochastic linkage between daily (here 500 hPa) CP types and daily precipitation events. Historical data and General Circulation Model (GCM) output of daily CPs corresponding to 1 × CO2 and 2 × CO2 are considered. Time series of both local and regional precipitation corresponding to each of those cases were simulated and their statistical properties were compared. Under the dry continental climate of eastern Nebraska, a highly variable spatial response to climate change was obtained. Most of the local and the regional average precipitation values reflect, under 2 × CO2, a somewhat wetter and a more variable precipitation regime in eastern Nebraska. The sensitivity of the results to the GCM utilized ...

A hydroclimatological model of areal drought

Abstract A stochastic hydroclimatological model is developed to characterize areal droughts. Statistical properties of drought indices are described by conditioning monthly drought indices on large-scale atmospheric circulation patterns (CP). The methodology is applied to an area of about 110 000 km 2 in eastern Nebraska, USA where the dry continental climate often produces severe droughts. In this example, the Bhalme-Mooley drought index (BMDI) illustrates the methodology. Five monthly classes of CP are derived using cluster analysis. Time series of BMDI are conditioned on monthly CP classes; BMDI is strongly related to these classes. A climate change may alter the structure of monthly CP classes; thus the impact of climate change on drought can be predicted.

Local temperature estimation under climate change

SummaryA methodology to estimate the space-time distribution of daily mean temperature under climate change is developed and applied to a central Nebraska case study. The approach is based on the analysis of the Markov properties of atmospheric circulation pattern (CP) types, and a stochastic linkage between daily (here 500hPa) CP types and daily mean temperatures. Historical data and general circulation model (GCM) output of daily CP corresponding to 1 × CO2 and 2 × CO2 scenarios are considered. The relationship between spatially averaged geopotential height of the 500 hPa surface — within each CP type — and daily mean temperature is described by a nonparametric regression technique. Time series of daily mean temperatures corresponding to each of these cases are simulated and their statistical properties are compared. Under the climate of central Nebraska, the space-time response of daily mean temperature to global climate change is variable. In general, a warmer climate appears to cause about 5°C increase in the winter months, a smaller increase in other months with no change in July and August. The sensitivity of the results to the GCM utilized should be considered.

Downscaling two versions of a general circulation model to estimate local hydroclimatic factors under climate change

Abstract The regional hydroclimatological effect of global climate change has been estimated and compared using a semi-empirical downscaling method with two versions (T21 and T42) of the general circulation model (GCM) developed at the Max Planck Institute for Meteorology, Germany. The comparisons were performed with daily mean temperature and daily precipitation amounts for the continental climate of the state of Nebraska, USA. Both the T21 and the T42 versions resulted in an increase of daily mean temperature under a 2 x C02 climatess. The magnitude of warming was substantially greater for T21 than for T42, except for February and June and at some stations in July where the T42 model suggested greater warming. Both GCMs resulted in a slight decrease in precipitation frequency and an increase in the amount of precipitation on wet days. Here, the T42 model again led to smaller changes. Different locations within Nebraska exhibited somewhat different temperature and precipitation responses with both GCM ve...

Water Quality in Coastal Zones under Variable Climatic Conditions

A methodology to investigate the influence of local hydrometeorological conditions on water quality in the coastal zone is developed, first under existing and then under changing climatic conditions. The methodological approach consists of two steps: (1) the local climatic characteristics, such as winds and temperature, are linked by a stochastic model to the global atmospheric circulation patterns (CP). By use of available data of daily CP in the Mediterranean, local meteorological conditions can be simulated. A conditional stochastic model is used for this linkage. The results of simulation of global circulation models may then be taken into consideration to obtain different local climatic conditions. (2) Meteorological parameters, such as wind and temperature obtained in the first step are used as controlling factors in a hydrodynamic model of coastal circulation coupled with an ecological model. As an example of application of this second step, the models have been used in the case of Thermaikos bay (N. Greece), where data are available. One of the main sources of pollution in the bay is sewerage from the city of Thessaloniki, which is to be treated to different degrees in the years to come. Results of the simulation can be used to recommend optimum sewerage treatment so as to minimize the risk of water pollution in the coastal zone under variable climatic conditions.

ESTIMATION OF LOCAL CLIMATIC FACTORS UNDER CLIMATE CHANGE

A stochastic space-time model for estimating the effect of global climate change scenarios on regional/local climatic factors is presented. Specifically, daily precipitation and temperature are considered. The approach is based on an analysis of daily atmospheric circulation patterns which are defmed as pressure surfaces using available data. A space-time model of local climatic factors is coupled with the stochastic simulation of daily circulation patterns in order to generate time series of point or areal daily precipitation and temperature reflecting regional/local effect of a global climate change. In addition to historical data, daily large-scale atmospheric pressure outputs of the Canadian Climate Center (CCC), Global Circulation Model (GCM) for the 1×CO2 and 2×CO2 cases are used. Some results from the Max Planck Institute, Hamburg, Germany (MPI) GCM are also considered for comparison purposes. The approach is based on the stochastic relationship between daily atmospheric circulation pattern (CP) types and daily climatic variables. Classification results of daily CPs cannot be used alone to measure changing spatial distribution of pressure heights obtained from GCMs and related to climate change. For this reason, an additional variable, the spatially averaged height of the 500 hPa pressure field is introduced into the analysis. The methodology appears to be sensitive to the choice of GCM. The methodology is then applied to the dry continental climate of Nebraska, U.S.A. The space-time response of daily mean temperature to global climate change is quite variable. In general, a warmer climate will imply about 5°C increase in the winter months, a smaller increase in other months with no change in July and August. On the other hand, most of the local and the areal precipitation reflect, under 2×CO2, a somewhat wetter and a more variable precipitation regime. This tendency is confirmed by using a common rainfall deficit-based drought index, the Bhalme-Mooley Drought Index.

Sediment Yield and Pollution from Small Watersheds under Changing Climatic Conditions

A stochastic simulation model is developed to estimate sediment yield and pollutant loadings from small agricultural watersheds under changing climatology. This model may be used whenever non-point pollution sources are needed to analyze me water quality of the receiving water body, such as a lake or a coastal area. The model is applied to compute the probability distribution of sediment yield, dissolved and sorbed phosphorus loading. The main input of the model, the daily precipitation is taken into account in three different climatic scenarios: (1) by use of historical daily time series data; (2) by use of the results of daily global atmospheric circulation patterns (CP) as given from a general circulation model (GCM) corresponding to 1×CO2. These latter results are linked to daily precipitation events by a stochastic Markov model; and (3) the same approach as in (2) but with the 2×CO2 scenario. The influence of a possible climatic change is compared in two different climate zones: (1) an agricultural watershed in the Great Plains area (Nebraska, USA) and (2) a coastal area in the Mediterranean (N. Aegean Sea, Greece).

Estimation of local and areal drought reflecting climate change

A methodology is developed and applied to eastern Nebraska to estimate the statistical properties of a meteorological drought index under climate change. The approach is based on the analysis of atmospheric circulation patterns (CP), followed by a stochastic linkage between daily CP types and daily local hydrometeorological quantities used to calculate the drought index. The methodology is illustrated with a common rainfall deficit-based drought index, the Bhalme-Mooley Drought Index (BMDI). Historical data and General Circulation Model (GCM) output of daily CP corresponding to the present (1xCO2) and doubled atmospheric CO2 (2xCO2) concentration are taken as inputs. Time series of both local and areal BMDI are simulated and their statistics are calculated. Under the dry continental climate of eastern Nebraska a highly variable spatial response to climate change has been obtained. Most of the local and the areal average drought indices reflect a somewhat wetter and a more variable climate under the 2xCO2 conditions. The results may be sensitive to the GCM used. The methodology can be used elsewhere to estimate statistically the impact of global climate change on local/regional drought.

Impact of Climatic Changes on Coastal Water Quality

Of considerable concern is the increase in atmospheric CO2 in recent years due to its influence on global climate. The studies so far have concentrated primarily on precipitation and air temperature changes from an assumed doubling in CO2 (the 2×CO2 scenario). For water resources, more specifically, climatic changes affect both quantity and quality. The effects on water quantity are presented in detail elsewhere in this volume and are not discussed here. The same applies also to other indirect substantial effects like changes in coastline, wetlands, etc., caused by sea level changes, flooding or erosion from coastal runoffs (Stakhiv et al., 1991).