Showing papers in "Climate Research in 1994"
TL;DR: In this article, Prentice et al. showed that the results of the climate-biome model are dependent on the initial land-surface conditions, and concluded that a biome model should be coupled with a climate model in the following way: firstly, the climate model is integrated over several years; secondly, a biome distribution should be computed from the corresponding multi-year simulated climatology; finally, land surface parameters are to be deduced from the biome distribution as boundary condition for a subsequent integration, and so on until an equilibrium is established.
Abstract: The BIOME model of Prentice et al. (1992; J. Biogeogr. 19: 117-134), which predicts global vegetation patterns in equilibrium with climate, was coupled with the ECHAM climate model of the Max-Planck-Institut fiir Meteorologie, Hamburg, Germany. It was found that incorporation of the BIOME model into ECHAM, regardless at which frequency, does not enhance the simulated climate variability, expressed in terms of differences between global vegetation patterns. Strongest changes are seen only between the initial biome distribution and the biome distribution computed after the first simulation period, provided that the climate-biome model is started from a biome distribution that resembles the present-day distribution. After the first simulation period, there is no significant shrinking, expanding, or shifting of biomes. Likewise, no trend is seen in global averages of land-surface parameters and climate variables. Significant differences in the results of the climate-biome model are found when single-year and multl-year climatologies are compared regardless whether climate and biome model are used in an off-line mode or are interactively integrated. It is concluded that a biome model should be coupled with a climate model in the following way: firstly, the climate model should be integrated over several years; secondly, a biome distribution should be computed from the corresponding multi-year simulated climatology; finally, land-surface parameters are to be deduced from the biome distribution as boundary condition of the climate model for a subsequent integration, and so on until an equilibrium is established. Starting the climate-biome model from a biome map which drastically differs from today's global distribution of biomes, but keeping present-day ocean temperatures fixed, it takes several iterations until the model finds a new equilibrium differing from the present-day vegetation distribution in certain parts of the globe. This study indicates that the results of the climatebiome model are dependent on the initial land-surface conditions.
123 citations
TL;DR: In this paper, the seasonal pattern of uptake and release of CO, by vegetation and soil in a steady-state climate simulation as well as the long-term development in a changing environment is presented.
Abstract: Within the global carbon cycle the world's ecosystenls are most sensitive to environmental change. We present a global model for calculating the seasonal pattern of uptake and release of CO, by vegetation and soil in a steady-state climate simulation as well as the long-term development in a changing environment. Within the terrestrial ecosystems 32 vegetation types are distinguished and combined with 7 distinct soil types with respect to their water-holding capacities. Within each vegetation type the llving b~omass is divided into 2 compartments, one with a short (seasonal) turnover containing the photosynthesizing tissue, feeder roots, and assinl~late store, and the other with a long turnover mainly consisting of structural plant material. The mathematical description is based on 2 hypotheses: (1) vegetation tends to maximize photosynthesizing tissue; and (2) a minimum amount of structural tissue is needed to support and maintain the product~ve parts, described by an allometric relation. The fluxes are modeled using standard equations for gross photosynthesis of the canopy, autotrophic respiration, and decomposition of dead organic matter depending on surface temperature, soil moisture, and irradiation. Within the system of differential equations the free parameters for each vegetation type are calibrated on the basis of a characteristic seasonal climate. In this paper the results of steady-state chmate experiments for the 2 vegetation types 'cold deciduous forest' and 'boreal forest' are compared with ecological measurements It was shown that the model yields satisfactory results with respect to phenology, gradients in net primary production, and standing b~omass and thus holds the promise to also yield good global results.
108 citations
TL;DR: In this article, a general, flexible method to assess changes in the local climatic inputs of ecosystem models from largescale climatic changes as simulated by general circulation models (GCMs) was developed, verified and applied at 5 Swiss locations for the summer and winter seasons.
Abstract: Based on the statistical approach proposed by von Storch et al. (1993 i J. Clim. 6: 1161-1171), a general, flexible method to assess changes in the local climatic inputs of ecosystem models from largescale climatic changes as simulated by general circulation models (GCMs) was developed, verified and applied at 5 Swiss locations for the summer and winter seasons. According to the requirements of various ecosystem models, at each location 17 seasonal statistics related to daily temperatures, precipitation, sunshine duration, air humidity and wind speed were considered. Year-to-year variations of the local variables were linked by means of Canonical Correlation Analysis to simultaneous anomalies in the North Atlantic/European sea-level pressure and near-surface temperature'fields. The analysis was performed for the period 1901 to 1940, separately for each season and location. In all cases, physically plausible statistical models were found which quantified the local effects of changes in major circulation patterns, such as the strength of westerly flow in winter and of large-scale subsidence in summer. In the verification interval 1941 to 1980, most variables were better reconstructed in winter than in summer, and better at the 3 north alpine than at the 2 south alpine locations. The best-reconstructed variables were seasonal mean daily temperatures and daily temperature extremes, for which on average 42 to 75 % of the total variances over the verification interval at the different locations could be explained. Explained variances for precipitation totals were 29 to 55% in winter and 10 to 28% in summer, and for mean daily relative sunshine durations in both seasons ca 12 to 530/0. Seasonal mean relative humidities, mean wind speeds, and within-summer standard deviations of daily variables were generally poorly reproduced. It was found that the procedure of von Storch et al. can be generally improved by using, in addition to sea-level pressure, the near-surface temperature as a large-scale predictor. Improvement was strongest for temperaturerelated variables, and for the summer season. The established statistical relationships were applied to anomaly fields as simulated by the Hamburg fully coupled atmospheric/oceanic ECHAM1/LSG GCM under increasing atmospheric greenhouse gas concentrations. The procedure yields time-dependent, internally consistent, and regionally strongly differentiated climatic change estimates for several important ecosystem inputs, at a spatial resolution far above the resolution of present GCMs.
105 citations
TL;DR: In this paper, the Arc-Info GIS and the PnET-IIS forest ecosystem model were used to predict and validate annual drainage and net primary productivity (NPP) on a 0.5° x 0° grid (approximately 50 x 75 km) for southern pine forests in the state of Georgia, USA.
Abstract: Regional-scale forest ecosystem models can provide insight into how forest hydrology and productivity differ in response to variations in climate and soil conditions. However, the databases necessary to define and validate these models are difficult to compile and utilize. The use of a Geographic Information System (GIS) can greatly simplify problems in database management. As an example of regional-scal e ecosystem model database development and model validation, the Arc-Info GIS and the PnET-IIS forest ecosystem model were used to predict and validate annual drainage and net primary productivity (NPP) on a 0.5° x 0.5° grid (approximately 50 x 75 km) for southern pine forests in the state of Georgia, USA. PnET-IIS is a lump-sum physiological model which used historic climate data from 1951 to 1984, along with soil water-holding capacity and species-specific vegetation charac- teristics. Annual predictions of drainage were well correlated with measured United States Geological Survey (USGS) drainage (r = 0.87, p < 0.0001), and predicted NPP was related with Forest Inventory Assessment (FIA) growth data collected across the state (r = 0.84, p < 0.0001). This study demonstrated the utility of a GIS in broad-scale ecosystem modeling and suggests that the need for model/GIS inter- facing in future research will continue to increase.
39 citations
TL;DR: The Lepage test, a nonparametnc 2-sample test, was used to statistically analyze annual and seasonal temperatures for the 344 climate divisions in the conterminous United States for the years 1895 through 1989 as discussed by the authors.
Abstract: In this study, the Lepage test, a nonparametnc 2-sample test, was used to statistically analyze annual and seasonal temperatures for the 344 climate divisions in the conterminous United States for the years 1895 through 1989 The objectives of the study were to (1) identify statistically significant changes in annual and seasonal temperatures, (2) determine durlng which seasons the most dramatic changes in temperature occurred, (3) de t e rm~ne the spatial distribution of significant changes in temperature and (4) determine whether significant regional temperature changes represented gradual or abrupt changes The analysis ind~cated that abrupt and significant changes in annual and seasonal temperatures occurred near 1930 and 1958, and that these changes occurred over large parts of the conterminous United States Othei significant and abrupt changes in seasonal temperatures occurred dunng the summer near 1917 and 1943, and during the autumn near 1948 and 1964 Temperatures durIng the summer season exhibited the most frequent and widespread signlf~cant changes The results of this study suggest that s~gnificant changes in reg~onal temperature occur abruptly as opposed to giadual uniform changes In a d d ~ t ~ o n , the changes In temperature were spat~al ly evtensive and appeal to be associated with changes in atmospheric c ~ r c u l a t ~ o n
35 citations
TL;DR: In this paper, the effect of anomalies in temperature and global radiation data on simulation results is studied for a spring wheat crop growth simulation model, and the results show that the use of averages resulted in overestimation of yield of up to 35% in some years.
Abstract: In weather data sets used by crop modellers, irregularities occur as inaccuracies in data or as missing values. In this investigation, the effect of such irregularities in temperature and global radiation data on simulation results is studied for a spring wheat crop growth simulation model. From the literature, the inaccuracy in temperature and global radiation data was estimated to be 1 °C and 10% respectively. Systematic over- or underestimation of the data using these values resulted in deviations in simulated yield of about 10%. Four methods of estimating missing values were compared: use of average values over 30 yr, over 1 mo and over 10 d, and use of daily data from another meteorological station. When all daily data were replaced by estimates, data from a nearby station gave the best results: only a small deviation in simulated yield was found. The use of averages resulted in overestimations of yield of up to 35% in some years. For global radiation data the effect of estimates based on sunshine duration data was also considered; use of these data gave a better result than data from a nearby station. When only 10% of the daily temperature and radiation data were replaced randomly by estimates, no effects on simulation results were found.
32 citations
TL;DR: In this paper, three spherically based interpolation methods, inversedistance weighting, triangulated surface patches, and thin-plate splines, are evaluated and compared using air temperature anomaly data.
Abstract: When observed air temperatures are analyzed spatially, irregularly sampled data are usually interpolated in some fashion. As a result, methods of spatial analysis clearly play a role in determining the size and variability of estimated air temperature changes, both spatially and temporally. Through graphical and statistical analysis, 3 spherically based interpolation methods inversedistance weighting, triangulated surface patches, and thin-plate splines are evaluated and compared using air temperature anomaly data. Analysis of errors resulting from spatial interpolation provides information about the strengths and weaknesses of historical station networks. Analysis of differences between the 3 interpolation methods suggests that similar spatial patterns are produced, but with some regional disparities. Mean absolute differences between interpolation methods can be over 0.4 "C for some sparse statlon networks and as low as O.l°C for dense station networks. When averaged spatially, however, the differences tend to offset one another, producing time series of terrestrial average air temperature anomalies that are largely independent of spatial interpolation method. Using cross validation to analyze spatial interpolation errors suggests that sparse station networks can produce nontrivial interpolation errors. Station networks from the late 1800s produce average interpolation errors of nearly 0.5"C, errors that are similar in magnitude to spatial standard deviations of air temperature anomalies. Denser station networks, typical of the 1950s and 1960s, produce average interpolation errors as low as 0.2OC. While these regional interpolation errors do not appear to influence estimates of terrestrial average air temperature, they do raise additional concerns regarding our ability to detect small climatic signals at regional scales.
30 citations
TL;DR: In this article, a statistical approach is developed based on the relationships between variations in regional climate and changes in large-scale circulation over the North Atlantic Ocean and Europe using long daily meteorological data sets at 3 weather stations in Germany.
Abstract: The investigation of impacts of a changed climate on social and natural systems usually requires daily meteorological data on a spatial scale of about 1 to 100 km2. Even the strongest tools for the estimation of the changes in future climate, the Global Climate Models (GCMs), are not yet able to provide regional scenarios of climate change at this resolution. Presently, GCMs operate at spatial resolutions of several hundred kilometers far too coarse for most regional analyses. Therefore, 'downscaling' approaches are needed in order to transform GCM climate change scenarios to appropriate scales for climate impact investigations. A statistical approach is developed based on the relationships between variations in regional climate and changes in large-scale circulation over the North Atlantic Ocean and Europe. Using the well-known classification scheme of circulation types, the European 'Grosswetterlagen', a conditional climatology for different large-scale air flow patterns was calculated using long daily meteorological data sets at 3 weather stations in Germany. The coupling of flow direction and climate at a site of interest varies with season and with c~rculation pattern. Even a single weather type produces relationships that are highly variable. A possible explanation could be the interaction of changes in mean duration time, preceding patterns or ~ h d r d ~ t e r i ~ t i ~ weather conditions caused by the weather type. To account for this natural variabil~ty an approach has been developed of stochastically modelling the linkage between daily weather type and regional daily weather Using long observed time series of daily surface air temperatures and precipitation, the model was calibrated and validated. Comparison of observed and simulated data demonstrated the ability of the model to adequately explain the statistical structure of the daily time series at 3 German sites. Possible applications of this model include sensitivity studies, downscaling of GCM output and investigation of the frequency and duration of extreme events.
30 citations
TL;DR: In this paper, the authors consider the problem of balancing the negative effects of CO2 emissions on a finite time horizon and propose two optimization approaches, which differ from earlier attempts to describe the interaction of economy and climate.
Abstract: A highly simplified time-dependent low-dimensional system was designed to describe conceptually the interaction of climate and economy. Enhanced emission of carbon dioxide (CO2) is understood as an agent that not only favors instantaneous consumption but also causes unfavorable climate changes at a later time. We consider the problem of balancing these 2 counterproductive effects of CO2 emissions on a finite time horizon. The climate system is represented by just 2 parameters, namely globally averaged near-surface air temperature and globally averaged tropospheric CO2 concentration. The costs of abating CO2 emissions are monitored by a function which depends quadratically on the percentage reduction of emission compared to an 'uncontrolled emission' scenario. Parameters are fitted to historical climate data and to estimates from studies of CO, abatement costs. Two optimization approaches, which differ from earlier attempts to describe the interaction of economy and climate, are discussed. In the 'cost-oriented' strategy an optimal emission path is identified which balances the abatement costs and explicitly formulated damage costs. These damage costs, the estimates of which are very uncertain, are hypothesized to be a linear function of the time derivative of temperature. In the 'target-oriented' strategy an emission path is chosen so that the abatement costs are minimal while certain restrictions on the terminal temperature and concentration change are met.
22 citations
TL;DR: In this paper, the effects of irregularities in weather data on simulated water-limited production of spring wheat are examined, using the same methods as described previously, in general the crop growth model used was not sensitive to inaccuracies in vapour pressure data and wind speed and average data for these variables could be used to replace missing values.
Abstract: In weather data sets used by crop modellers irregularities occur as inaccuracies in given data and as missing values. The effects of irregularities in temperature and global radiation data on potential production of spring wheat were discussed previously. Here the effects of irregularities in weather data on simulated water-limited production of spring wheat are examined, using the same methods as described previously. In general the crop growth model used was not sensitive to inaccuracies in vapour pressure data and wind speed and average data for these variables could be used to replace missing values. The sensitivity of the model to inaccuracies in other weather data depended on the amount of water available to the crop. In dry years the model was sensitive to inaccuracies in precipitation and radiation data but less so to inaccuracies in air temperature. When water was not limiting the model was not sensitive to inaccuracies in precipitation, but was sensitive to inaccuracies in temperature and radiation data. Use of average values for temperature and global radiation led to large deviations in simulation results. For all variables except precipitation, data from a nearby weather station represented good estimates for missing values. Rainfall data for estimations should be obtained from a site in the immediate vicinity. However, when the complete data set (i.e. for all weather variables) from a station 40 km away was used as input for the model, deviations of up to 2 t ha-1 (= 30%) in simulated yields were found.
14 citations
TL;DR: In this article, the authors examined land surface variability within a (100 km)2 grid cell (about 1\" of latitude by 1' of longitude) using a 3D mesoscale atmosphere-land surface model.
Abstract: Numerical and observational studies have documented the climatic importance of land surface variability, especially in soil moisture and surface roughness. Land surface representations within global c h a t e models, however, have been based on global vegetation data sets that characterize the surface as invariant at scales smaller than about (100 km12. Within climate model grid areas, the vegetation data are spatially aggregated once more to areas on the order of 105 km2 and are assigned to a single, spatially homogeneous vegetation type. In this study, we examine land surface variability within a (100 km)2 grid cell (about 1\" of latitude by 1' of longitude) using a 3-dimensional mesoscale atmosphere-land surface model. Our investigation focuses on changes in the coverage and geographic pattern of irrigated maize (corn) and non-irrigated bare soil. Maize and bare soil are among the most disparate land covers that occur together in the present-day landscape and they exemplify highly heterogeneous spatial mosaics. Our simulations indicate that increasing the area of bare soil downwind of irrigated maize produces a nearly linear increase in daily average surface temperatures, along with a linear decrease in the average latent heat flux. Bare soil upwind of irrigated maize, however, forces a more nonlinear response. The largest effects occur when small areas of bare soil are introduced into the domain. Simulations with several mosaics containing 50 % irrigated maize and 50 % bare soil also suggest that changes in the spatial arrangement of the land surface alone can result in differences in areaaveraged surface temperatures and near-surface air temperatures of up to 1 \"C. Our results additionally suggest that subgrid-scale area weighting schemes should yield better surface representations than the assignment of a single homogeneous surface type. It also appears that subgrid-scale area weighting may need to account for the spatial distribution of the vegetation within a grid cell.
TL;DR: In this paper, the authors used a simple model of geographic coordinates and elevation to estimate the urban-rural mean temperature in northeastern Argentina and found that the urban heat island effect is now being masked to a certain extent by global warming, contrary to what happened during the first 60 years of this century.
Abstract: The yearly mean temperature in northeastern Argentina is described by a simple model of geographic coordinates and elevation. According to the simulation tests, the mean temperature in a given year at any rural location can be estimated with a RMSE (root-mean-square error) of less than 0.5"C as long as there are data from at least 8 stations in the region. The mean temperature for a 5 yr period can be estimated with an even lower RMSE of less than 0.4 "C. In spite of the severe limitations of the available data, use of the model permits Buenos Aires' urban-rural temperature differences since 1929 to be calculated. During the 1940s and 1950s the urban-rural temperature difference increased linearly, later slowing and reaching a maximum during the 1960s. The decrease in the urban-rural temperature difference after the 1960s could be attributed at least in part to the general warming of the region. An extension of the latter finding to global surface mean temperature suggests the possibility that the presumed global warming is now being masked to a certain extent by the urban heat island effect, contrary to what happened during the first 60 yr of this century. K E Y WORDS: Urban heat island Global warming . Temperature
TL;DR: In this article, the authors reexamine design storms of 24-hour duration in Louisiana using data with much longer periods of record and compare with frequency-magnitude relationships from TP40.
Abstract: For the last 30 yr or more, National Weather Bureau Technical Paper No. 40 (TP40) has been the primary document providing design rainstorm magnitudes for the conterminous United States. The primary lunitation of this document today is the short periods of record analyzed to determine the design storm estimates. Therefore, this paper reexamines design storms of 24 h duration in Louisiana using data with much longer periods of record. Results were then compared with frequencymagnitude relationships from TP40. The magnitudes of the storm recurrence intervals were found to differ between the 2 estimations, particularly for the longer durations. Furthermore, greater geographical detail emerged relative to the generalized patterns depicted in TP40.
TL;DR: In this article, a method of interpolating to a fine grid is proposed, using a regression model of station precipitation related to geographical variables of latitude, longitude, and elevation.
Abstract: Issues in the accuracy of preparing and using isoline maps of mean precipitation are reconsidered. A simulation is created to illustrate the concepts of discontinuous distribution and the effects of sampling on the resulting maps. The actual rainfall field is more closely represented as the number of rainfall events considered and the grid density increase. Data from Israel are used to provide concrete illustrations. A method of interpolating to a fine grid is proposed, using a regression model of station precipitation related to geographical variables of latitude, longitude, and elevation.
TL;DR: In this paper, a regression model is developed based on the relationship between the Pacific-North American (PNA) teleconnection pattern, represented by a PNA index, and surface temperature anomaly f~eld across the continental United States.
Abstract: The Pacific-North American (PNA) teleconnection pattern is one of the most prominent teleconnection patterns in the Northern Hemisphere winter It manifests as anomalies in the 700 or 500 rnb geopotential height field and is closely related to the upper-level flow patterns and surface temperature and precipitation conditions in the United States. Various indices were developed to represent the strength of the PNA teleconnection. However, their apphcation in climatological research CLIMATE RESEARCH Clim. Res. is limited by a short record length as continuous upper-level measurements are widely available only since 1947. The purpose of this study is to reconstruct the winter PNA pattern for the period 1895 to 1947. A regression model is developed based on the relationship between the PNA pattern, represented by a PNA index, and surface temperature anomaly f~eld across the continental United States. The model can explain over 89 % of the variance in the PNA Index and offers unbiased estimates. Using this model, the winter PNA index is extended back to the 1895-96 winter. Analysis of winter precipitation anomalies in the United States from 1895 to 1988 illustrates the application potential of this extended record. With a longer record length, more extreme cases of the PNA teleconnection can be identified to analyze its relation to surface conditions. The extended PNA index is useful in explaining the cooling trend from the 1940s to the 1970s in the eastern United States. It is also significantly related to an extended Southern Oscillation index from 1895 to 1988. However, the relationship is not strong enough to suggest a completely synchronized occurrence of the 2 teleconnection patterns.
TL;DR: The 6180 values of daytime total leaf water and leaf cellulose in yellow-poplar Liriodendron tulipifera (L.) (Magnoliaceae) were elevated approximately 2%0 when exposed to ambient + 300 pm01 mol-' CO2 in outdoor open-top chambers relative to ambient CO2 concentrations as mentioned in this paper.
Abstract: The 6180 values of daytime total leaf water and leaf cellulose In yellow-poplar Liriodendron tulipifera (L.) (Magnoliaceae) were elevated approximately 2%0 when exposed to ambient + 300 pm01 mol-' CO2 in outdoor open-top chambers relative to ambient CO2 concentrations. In growth chambers, 6180 values of total leaf water during the light cycle were also ennched in a droughtstressed, 700 pm01 mol-' CO2 atmosphere treatment with lmited changes in humid~ty. In both experiments, no significant differences were observed among treatments in SD values of leaf water in ambient or elevated CO2. Simultaneous isotopic measurements made of Quercus alba (L.), also growing in the same open-top chambers, did not show any significant difference among treatments in either 6180 or SD values in leaf water for ambient, ambient + 150 or ambient + 300 pm01 mol-' CO2 treatments. Differences between the species that may be important to this variable response include apparent longer water-turnover times in L. tulipifera that were inferred from stomata1 conductance and leaf water content measurements. The increases in S1'O values of leaf water in L. tulipifera without parallel increases in SD values indicate that elevated CO2 treatments can affect the kinetics of leaf water evapotranspiration, for which H:80 evaporation is more sensitive than DHI60. The apparent long-term incorporation of this increase in SI80 values of daytime total leaf water into leaf cellulose of L. tuliplfera may introduce a new compllcatlon into the use of stable isotopes ot toss~llzed plant f~bers as paleoclimat~c indicators because of the long-term var~a t~on In atmospheric CO2 content. KEY WORDSOxygen isotopes . Carbon dioxide enrichment . Hydrogen isotopes . Isotopic content of leaf water . Isotopic content of cellulose . Paleorlimate
TL;DR: In this article, the authors examined the relationship between seasonal 700 mb height data and precipitation data in the western Great Basin, USA using bivariate regression and found that approximately one-third of precipitation variability in the study region is associated with changes in 700mb heights during the autumn, winter, and spring months, but little associated variance occurs during summer.
Abstract: Associations between seasonal 700 mb height data and precipitation data in the western Great Basin, USA, are examined from 1947 through 1991 using bivariate regression. Results show that approximately one-third of precipitation variability in the study region is associated with changes in 700 mb heights during the autumn, winter, and spring months, but little associated variance occurs during summer. When only wet seasons are examined (2 scores >0.5), correlations increase substantially However, dry season ( Z scores c-0.5) data correlations had little interpretive value.