Synoptic aspects of wet and dry conditions in central Australia: observations and GCM simulations for 1 × CO2 and 2 × CO2 conditions

Synoptic climatological patterns that produce anomalous wet conditions in central Australia during the period from September to April have been studied. The analysis was done by using observed daily rainfall data at a number of stations, wind and mean sea level pressure from the European Centre for Medium Range Weather Forecasts (ECMWF), Tropical Ocean and Global Atmosphere (TOGA) data from 1985 to 1991, and the CSIRO 9-level (CSIR09) global climate model (GCM) simulated data for 1 × CO₂ and 2 × CO₂ experiments. On the basis of rainfall values above 99.5 percentile in observed and simulated data, wet days have been selected to study the synoptic-scale weather systems that produce anomalous wet events in central Australia. As the vast majority of days in central Australia are dry, the same number of days with no rainfall for both observed and simulated conditions have been selected randomly. The observed synoptic climatological patterns have been compared with the results of the control simulation of CSIRO9. A comparison between CSIRO9 simulated synoptic patterns and observed synoptic patterns reveals that the model fairly well captures the synoptic climatological characteristics which produce anomalous wet and contrasting dry weather conditions during the period from September to April. Under enhanced greenhouse experiments, the main features of the synoptic patterns are intensified both for wet and dry conditions, which result in an increase in extreme weather conditions, an increase in rainfall intensity, a spatial expansion of the heavy rainfall region during wet days, and an expansion of the dry area during dry days. During anomalous wet conditions, the low pressure area is intensified, monsoonal winds and southeasterlies are strengthened and strong wind shear over tropical Australia is simulated. During this condition, the monsoon shear line moves poleward particularly over the Northern Territory. In contrast, during dry conditions, the anticyclonic circulation over the continent is strengthened.     

Implications of climate change due to the enhanced greenhouse effect on floods and droughts in Australia

Potential impacts of climate change on heavy rainfall events and flooding in the Australian region are explored using the results of a general circulation model (GCM) run in an equilibrium enhanced greenhouse experiment. In the doubled CO₂ simulation, the model simulates an increase in the frequency of high-rainfall events and a decrease in the frequency of low-rainfall events. This result applies over most of Australia, is statistically more significant than simulated changes in total rainfall, and is supported by theoretical considerations. We show that this result implies decreased return periods for heavy rainfall events. The further implication is that flooding could increase, although we discuss here the many difficulties associated with assessing in quantitative terms the significance of the modelling results for the real world.The second part of the paper assesses the implications of climate change for drought occurrence in Australia. This is undertaken using an off-line soil water balance model driven by observed time series of rainfall and potential evaporation to determine the sensitivity of the soil water regime to changes in rainfall and temperature, and hence potential evaporation. Potential impacts are assessed at nine sites, representing a range of climate regimes and possible climate futures, by linking this sensitivity analysis with scenarios of regional climate change, derived from analysis of enhanced greenhouse experiment results from five GCMs. Results indicate that significant drying may be limited to the south of Australia. However, because the direction of change in terms of the soil water regime is uncertain at all sites and for all seasons, there is no basis for statements about how drought potential may change.     

Quantifying uncertainty in changes in extreme event frequency in response to doubled CO2 using a large ensemble of GCM simulations

We discuss equilibrium changes in daily extreme surface air temperature and precipitation events in response to doubled atmospheric CO₂, simulated in an ensemble of 53 versions of HadSM3, consisting of the HadAM3 atmospheric general circulation model (GCM) coupled to a mixed layer ocean. By virtue of its size and design, the ensemble, which samples uncertainty arising from the parameterisation of atmospheric physical processes and the effects of natural variability, provides a first opportunity to quantify the robustness of predictions of changes in extremes obtained from GCM simulations. Changes in extremes are quantified by calculating the frequency of exceedance of a fixed threshold in the 2 × CO₂ simulation relative to the 1 × CO₂ simulation. The ensemble-mean value of this relative frequency provides a best estimate of the expected change while the range of values across the ensemble provides a measure of the associated uncertainty. For example, when the extreme threshold is defined as the 99th percentile of the 1 × CO₂ distribution, the global-mean ensemble-mean relative frequency of extremely warm days is found to be 20 in January, and 28 in July, implying that events occurring on one day per hundred under present day conditions would typically occur on 20–30 days per hundred under 2 × CO₂ conditons. However the ensemble range in the relative frequency is of similar magnitude to the ensemble-mean value, indicating considerable uncertainty in the magnitude of the increase. The relative frequencies in response to doubled CO₂ become smaller as the threshold used to define the extreme event is reduced. For one variable (July maximum daily temperature) we investigate this simulated variation with threshold, showing that it can be quite well reproduced by assuming the response to doubling CO₂ to be characterised simply as a uniform shift of a Gaussian distribution. Nevertheless, doubling CO₂ does lead to changes in the shape of the daily distributions for both temperature and precipitation, but the effect of these changes on the relative frequency of extreme events is generally larger for precipitation. For example, around one-fifth of the globe exhibits ensemble-mean decreases in time-averaged precipitation accompanied by increases in the frequency of extremely wet days. The ensemble range of changes in precipitation extremes (relative to the ensemble mean of the changes) is typically larger than for temperature extremes, indicating greater uncertainty in the precipitation changes. In the global average, extremely wet days are predicted to become twice as common under 2 × CO₂ conditions. We also consider changes in extreme seasons, finding that simulated increases in the frequency of extremely warm or wet seasons under 2 × CO₂ are almost everywhere greater than the corresponding increase in daily extremes. The smaller increases in the frequency of daily extremes is explained by the influence of day-to-day weather variability which inflates the variance of daily distributions compared to their seasonal counterparts.     

Simulation of the variability and extremes of daily rainfall during the Indian summer monsoon for present and future times in a global time-slice experiment

In this study the simulation of the variability and extremes of daily rainfall during the Indian summer monsoon for the present-day and the future climate is investigated. This is done on the basis of a global time-slice experiment (TSL) with the ECHAM4 atmospheric general circulation model (GCM) at a high horizontal resolution of T106. The first time-slice (period: 1970–1999) represents the present-day climate and the second (2060–2089) the future climate. Moreover, observational rainfall data from the Global Precipitation Climatology Project (GPCP, 1997–2002) and rainfall data from the ECMWF re-analysis (ERA, 1958–2001) are considered. ERA reveals serious deficiencies in its representation of the variability and extremes of daily rainfall during the Indian summer monsoon. These are mainly a severe overestimation of the frequency of wet days over the oceans and in the Himalayas, where also the rainfall intensity is overestimated. Further, ERA shows unrealistically heavy rainfall events over the tropical Indian Ocean. The ECHAM4 atmospheric GCM at a horizontal resolution of T106, on the other hand, simulates the variability and extremes of daily rainfall in good agreement with the observations. The only marked deficiencies are an underestimation of the rainfall intensity on the west coast of the Indian peninsula and in Bangladesh, an overestimation over the tropical Indian Ocean, due to an erroneous northwestward extension of the tropical convergence zone, and an overestimation of the frequency of wet days in Tibet. Further, heavy rainfall events are relatively strong in the centre of the Indian peninsula. For the future, TSL predicts large increases in the rainfall intensity over the tropical Indian Ocean as well as in northern Pakistan and northwest India, but decreases in southern Pakistan, in the centre of the Indian peninsula, and over the western part of the Bay of Bengal. The frequency of wet days is markedly increased over the tropical Indian Ocean and decreased over the northern part of the Arabian Sea and in Tibet. The intensity of heavy rainfall events is generally increased in the future, with large increases over the Arabian Sea and the tropical Indian Ocean, in northern Pakistan and northwest India as well as in northeast India, Bangladesh, and Myanmar.     

Changes in daily precipitation under enhanced greenhouse conditions

 An increase in global average precipitation of about 10% is simulated by two global climate models with mixed layer oceans in response to an equilibrium doubling of carbon dioxide. The UKHI model was developed in the United Kingdom at the Hadley Centre for Climate Prediction and Research and the CSIRO9 model was developed in Australia by the CSIRO Division of Atmospheric Research. Regional changes in daily precipitation simulated by these models have been compared. Both models simulate fewer wet days in middle latitudes, and more wet days in high latitudes. At middle and low latitudes, there is a shift in the precipitation type toward more intense convective events, and fewer moderate non-convective events. At high latitudes, the precipitation type remains non-convective and all events simply get heavier, resulting in fewer light events and more moderate and heavy events. The probability of heavy daily precipitation increases by more than 50% in many locations. Extreme events with a probability of 1% or less were considered in terms of return periods (the average period between events of the same magnitude). For a given return period of at least 1 y, precipitation intensity in Europe, USA, Australia and India increases by 10 to 25%. For a given precipitation intensity, the average return period becomes shorter by a factor of 2 to 5. Given that larger changes in frequency occur for heavier simulated events, changes may be even greater for more-extreme events not resolved by models. Received: 1 July 1996 / Accepted: 21 March 1997     

The Potential Impact of Global Warming on Hail Losses to Winter Cereal Crops in New South Wales

This study was undertaken to determine the impact of potential global warming on the magnitude of hail losses to winter cereal crops within two areas situated on the western slopes of New South Wales, Australia. A model relating historical crop hail losses to climatic variables was developed for each area. These models included seasonal measures of vertical instability, low-level moisture and the height of the freezing level. In both areas, windshear was not found to be an important factor influencing seasonal crop hail losses. The two crop hail loss models were then used in conjunction with upper-air climatic data from three single mixed-layer global climate models (GCMs). Each GCM was run for 1 × CO₂ conditions and for 2 × CO₂ conditions. The enhanced greenhouse effect on climatic variables was taken to be the difference between their values for these two runs. Changes to climatic variables were then translated directly into changes in the percentage value of the winter cereal crop lost due to hail. In both areas, the three GCMs agreed concerning the direction of change in each of the variables used in the crop hail loss model. GCM simulations of the greenhouse effect resulted in a decline in winter cereal crop hail losses, with the exception of one GCM simulation at one location where losses increased slightly. None of the changes due to the enhanced greenhouse effect, however, were significant owing to a large observed seasonal variability of crop hail losses. Also, the simulated seasonal variability of crop hail losses did not change significantly due to the enhanced greenhouse effect. These results depended on two important assumptions. Firstly, it was assumed that the dominant relationships between climatic variables and crop hail losses in the past would remain the same in a future climate. Secondly, it was assumed that the single mixed-layer GCMs used in the study were correctly predicting climate change under enhanced greenhouse conditions.     

Tropical disturbances in a GCM

We have analyzed the tropical disturbances in a 11-layer atmospheric general circulation model (GCM) on a 2.5° × 3.75° horizontal grid coupled to a 50 m-mixed layer ocean. Due to the coarse resolution, the GCM is unable to resolve adequately tropical cyclones. The tropical disturbances simulated by the GCM are much weaker and have a much larger horizontal extent. However, they still display much of the essential physics of tropical cyclones, including low-level convergence of mass and moisture, upper tropospheric outflow and a warm core. For most ocean basins the spatial and temporal distribution of the simulated tropical disturbances compares well with the observed tropical cyclones. On doubling the CO₂ concentration, the number of simulated tropical disturbances increases by about 50%. There is a relative increase in the number of more intense tropical disturbances, whose maximum windspeed increases by about 20%. This agrees with the theoretical estimate of Emanuel. However, because the low-resolution of the GCM severely restricts their maximum possible intensity, simulated changes in tropical disturbance intensity should be interpreted cautiously.     

Simulated Climate Change Effects on Year-Round Water Temperatures in Temperate Zone Lakes

A deterministic, one-dimensional model is presented to simulate daily water temperature profiles and associated ice and snow covers for dimictic and polymictic lakes of the temperate zone. The lake parameters required as model input are surface area (A_s), maximum depth (H_MAX), and Secchi depth (z_s), the latter, used as a measure of light attenuation and trophic state. The model is driven by daily weather data and operates year-round over multiple years. The model has been tested with extensive data (over 5,000 temperature points). Standard error between simulated and measured water temperatures is 1.4°C in the open water season and 0.5°C in the ice cover season. The model is applied to simulate the sensitivity of Minnesota lake water temperature characteristics to climate change. The projected climate changes due to a doubling of atmospheric CO₂ are obtained from the output of the Canadian Climate Center General Circulation Model (CCC GCM) and the Goddard Institute of Space Studies General Circulation Model (GISS GCM). Simulated lake temperature characteristics have been plotted in a coordinate system with a lake geometry ratio (A _s ^0.25 /H_MAX) on one axis and Secchi depth on the other. The lake geometry ratio expresses a lake's susceptibility to stratification. By interpolation, the sensitivity of lake temperature characteristics to changes of water depth and Secchi depth under the projected climate scenarios can therefore be obtained. Selected lake temperature characteristics simulated with past climate conditions (1961–1979) and with a projected 2 × CO₂ climate scenario as input are presented herein in graphical form. The simulation results show that under the 2 × CO₂ climate scenario ice formation is delayed and ice cover period is shortened. These changes cause water temperature modifications throughout the year.     

Climatic change over the Lowveld of South Africa

There has been a 38% decrease in expected annual rainfall totals over the Lowveld, in the eastern part of South Africa, during the last two decades. The downward trend in mean annual rainfall is not replicated in the rest of the summer rainfall region above the escarpment. Rainfall variability over the Lowveld has been increasing since about the 1950s, although the increase in variability appears to have been slowing down in more recent years. Changes in the frequency and intensity of El Niño/Southern Oscillation extreme events are only partly responsible for the observed desiccation and increase in rainfall variability. The CSIRO 9-level general circulation model simulates, for 2 × CO₂ conditions, an insignificant decrease of 10% in the annual mean and a slight increase in the inter-annual variability of rainfall over the Lowveld. Other general circulation models likewise simulate only small changes in annual mean rainfall over the region. However, the simulated increase in rainfall variability by the CSIRO 9-level model is likely to be conservative since the model, being linked to a slab ocean, is unable to represent important features of ocean-atmosphere coupling in the region. Significant changes in the frequencies of extreme drought events and of heavy rains in the Lowveld are likely to occur even with only small changes in the rainfall climatology of the region.     

A projection of extreme climate events in the 21st century over east Asia using the community climate system model 3

A series of coupled atmosphere-ocean-land global climate model (GCM) simulations using the National Center for Atmospheric Research (NCAR) Community Climate System Model 3 (CCSM3) has been performed for the period 1870–2099 at a T85 horizontal resolution following the GCM experimental design suggested in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). First, a hindcast was performed using the atmospheric concentrations of three greenhouse gases (CO₂, CH₄, N₂O) specified annually and globally on the basis of observations for the period 1870–1999. The hindcast results were compared with observations to evaluate the GCM’s reliability in future climate simulations. Second, climate projections for a 100-year period (2000–2099) were made using six scenarios of the atmospheric concentrations of the three greenhouse gases according to the A1FI, A1T, A1B, A2, B1, and B2 emission profiles of the Special Report on Emissions Scenarios. The present CCSM simulations are found to be consistent with IPCC’s AR4 results in the temporal and spatial distributions for both the present-day and future periods. The GCM results were used to examine the changes in extreme temperatures and precipitation in East Asia and Korea. The extreme temperatures were categorized into warm and cold events: the former includes tropical nights, warm days, and heat waves during summer (June–July–August) and the latter includes frost days, cold days, and cold surges during winter (December–January–February). Focusing on Korea, the results predict more frequent heat waves in response to future emissions: the projected percentage changes between the present day and the late 2090s range from 294% to 583% depending on the emission scenario. The projected global warming is predicted to decrease the frequency of cold extreme events; however, the projected changes in cold surge frequency are not statistically significant. Whereas the number of cold surges in the A1FI emission profile decreases from the present-day value by up to 24%, the decrease in the B1 scenario is less than 1%. The frequency and intensity of extreme precipitation events year-round were examined. Both the frequency and the intensity of these events are predicted to increase in the region around Korea. The present results will be helpful for establishing an adaptation strategy for possible climate change nationwide, especially extreme climate events, associated with global warming.     

Regional climate changes as simulated in time-slice experiments

Three 30 year long simulations have been performed with a T42 atmosphere model, in which the sea-surface temperature (SST) and sea-ice distribution have been taken from a transient climate change experiment with a T21 global coupled ocean-atmosphere model. In this so-called time-slice experiment, the SST values (and the greenhouse gas concentration) were taken at present time CO₂ level, at the time of CO₂ doubling and tripling.The annual cycle of temperature and precipitation has been studied over the IPCC regions and has been compared with observations. Additionally the combination of temperature and precipitation change has been analysed. Further parameters investigated include the difference between daily minimum and maximum temperature, the rainfall intensity and the length of droughts.While the regional simulation of the annual cycle of the near surface temperature is quite realistic with deviations rarely exceeding 3 K, the precipitation is reproduced to a much smaller degree of accuracy.The changes in temperature at the time of CO₂ doubling amount to only 30–40% of those at the 3 * CO₂ level and show hardly any seasonal variation, contrary to the 3 * CO₂ experiment. The comparatively small response to the CO₂ doubling can be attributed to the cold-start of the simulation, from which the SST has been extracted. The strong change in the seasonality cannot be explained by internal fluctuations and cold start alone, but has to be caused by feedback mechanisms. Due to the delay in warming caused by the transient experiment, from which the SST has been derived, the 3 * CO₂ experiment can be compared to the CO₂ doubling studies performed with mixed-layer models.The precipitation change does not display a clear signal. However, an increase of the rain intensity and of longer dry periods is simulated in many regions of the globe.The changes in these parameters as well as the combination of temperature- and precipitation change and the changes in the daily temperature range give valuable hints, in which regions observational studies should be intensified and under which aspects the observational data should be evaluated.     

Regional Nested Model Simulations of Present Day and 2 × CO2 Climate over the Central Plains of the U.S.

A nested regional climate model is used to generate a scenario of climate change over the MINK region (Missouri, Iowa, Nebraska, Kansas) due to doubling of carbon dioxide concentration (2 × CO₂) for use in agricultural impact assessment studies. Five-year long present day (control) and 2 × CO₂ simulations are completed at a horizontal grid point spacing of 50 km. Monthly and seasonal precipitation and surface air temperature over the MINK region are reproduced well by the model in the control run, except for an underestimation of both variables during the spring months. The performance of the nested model in the control run is greatly improved compared to a similar experiment performed with a previous version of the nested modeling system by Giorgi et al. (1994). The nested model generally improves the simulation of spatial precipitation patterns compared to the driving general circulation model (GCM), especially during the summer. Seasonal surface warming of 4 to 6 K and seasonal precipitation increases of 6 to 24% are simulated in 2 × CO₂ conditions. The control run temperature biases are smaller than the simulated changes in all seasons, while the precipitation biases are of the same order of magnitude as the simulated changes. Although the large scale patterns of change in the driving GCM and nested RegCM model are similar, significant differences between the models, and substantial spatial variability, occur within the MINK region.     

Local temperature estimation under climate change

Summary A 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.On leave from the Department of Meteorology, Eötvós Loránd University, Budapest, Hungary.With 14 Figures     

Climatological features of blocking anticyclones: a study of Northern Hemisphere CCM1 model blocking events in present-day and double CO2 concentration atmospheres

 Using output made with the National Center for Atmospheric Research (NCAR) Community Climate Model Version 1 (CCM1), the characteristics of blocking events over the Northern Hemisphere in a ten-year present day control simulation with a CO₂ concentration of 330 ppm were compared to those in a previously analyzed observational three-year climatology. The characteristics of blocking events in a double present-day CO₂ concentration simulation were then compared to those in the control simulation in order to evaluate how these characteristics might change in an increased CO₂ atmosphere. The results demonstrated that in the Northern Hemisphere the CCM1 correctly simulated many characteristics of blocking events such as average annual number of occurrences, annual variations is size and intensity, and preferred formation regions. A more detailed analysis (i.e., by region and season) revealed some differences between the CCM1 and observed blocking events for characteristics such as mean frequency of occurrence, intensity, size and duration. In addition, the model failed to capture adequately the occurrence of blocking events over the western Asian continent. A comparison of the double CO₂ concentration run to the control showed that, in general, blocking events were more persistent and weaker, but of similar size in the increased CO₂ atmosphere. Also, some statistically significant regional and seasonally dependent changes were found in the frequency of occurrence, duration, and intensity. Finally, a correlation between block size and intensity, significant at the 99% confidence level, was found in each climatology. This result is similar to a correlation found in the analysis of observations. Received: 8 May 1995 / Accepted: 20 September 1996     

Climate Change and People-Caused Forest Fire Occurrence in Ontario

Climate change that results from increasing levels of greenhouse gases in the atmosphere has the potential to increase temperature and alter rainfall patterns across the boreal forest region of Canada. Daily output from the Canadian Climate Centre coupled general circulation model (GCM) and the Hadley Centre's HadCM3 GCM provided simulated historic climate data and future climate scenarios for the forested area of the province of Ontario, Canada. These models project that in climates of increased greenhouse gases and aerosols, surface air temperatures will increase while seasonal precipitation amounts will remain relatively constant or increase slightly during the forest fire season. These projected changes in weather conditions are used to predict changes in the moisture content of forest fuel, which influences the incidence of people-caused forest fires. Poisson regression analysis methods are used to develop predictive models for the daily number of fires occurring in each of the ecoregions across the forest fire management region of Ontario. This people-caused fire prediction model, combined with GCM data, predicts the total number of people-caused fires in Ontario could increase by approximately 18% by 2020–2040 and50% by the end of the 21st century.     

Daily rainfall trend in the Valencia Region of Spain

Summary ?We have analyzed daily rainfall trends throughout the second half of the 20^th century in the western Mediterranean basin (Valencia Region, E of Spain). The area is characterized by high torrentiality, and during the second half of the 20^th century some of the highest daily rainfall values in the Mediterranean basin have been recorded. In this area, mean annual rainfall varies between 500 and 300 mm and is overwhelmingly dependent on just a few days of rain. Daily maximum rainfall varies on average from 120 mm day⁻¹ to 50 mm day⁻¹, and represents a mean of 17% (coastland) to 9% (inland) of annual rainfall. The 10 days in each year with the heaviest rainfall (called “higher events”) provide over 50% of the annual rainfall and can reach more than 400 mm on average. We compared the annual rainfall trend and the trend of higher and minor events defined by percentiles, both in volume and variability. We, therefore, tested whether annual rainfall changes depend on the trend of the higher (rainfall) events. To overlap spatial distribution of trends (i.e.: positive, no significant and negative trends) we have used cross-tab analysis. The results confirm the hypothesis that annual rainfall changes depend on changes found in just a few rainy events. Furthermore, in spite of their negative trend, higher events have increased their contribution to annual rainfall. As a consequence, although torrential events may have diminished in magnitude, future scenarios seem to be controlled by a limited number of rainy events which will become more and more variable year on year. The high spatial density of data used in this work, (97 observatories per 24.000 km², overall mean 1 observatory per 200 km²), suggests to us that extreme caution should be applied when analyzing regional and sub-regional changes in rainfall using GCM output, especially in areas of high torrentiality. Received August 1, 2002; revised November 11, 2002; accepted December 1, 2002 Published online May 19, 2003     

Effect of climate change on regional precipitation in Lake Balaton watershed

Summary A methodology is developed and applied to the area of Lake Balaton and its drainage basin, a region of Western Hungary, to estimate the space-time distribution of daily precipitation under climate change. Lake Balaton is the largest lake in Central and Western Europe; it has a central location in the country and its drainage basin covers about the 20% of Hungary (together with the Sió Canal). The methodology is based on an analysis of the semi-Markovian properties of atmospheric macrocirculation pattern types (MCP), and a stochastic linkage between daily (here 700 hPa) MCP types and daily precipitation events. Historical data and General Circulation Model (GCM) output of daily MCP corresponding to 1 · CO₂ and 2 · CO₂ scenarios are considered in this study. Time series of both local and areal precipitation corresponding for both scenarios are simulated and their statistical properties are compared. For the temperate continental climate of Western Hungary a slightly variable spatial response to climate change is obtained. Under 2 · CO₂ conditions most of the local and the areal average precipitation suggests, a somewhat dryer precipitation regime in Western Hungary. The sensitivity of the results to the GCM utilized should be considered.With 10 Figures     

Using a GCM analogue model to investigate the potential for Amazonian forest dieback

Summary A combined GCM analogue model and GCM land surface representation is used to investigate the influences of climatology and land surface parameterisation on modelled Amazonian vegetation change. This modelling structure (called IMOGEN) captures the main features of the changes in surface climate as estimated by a GCM with enhanced atmospheric greenhouse gas concentrations. Advantage is taken of IMOGENs computational speed which allows multiple simulations to be carried out to assess the robustness of the GCM results.The timing of forest dieback is found to be sensitive to the initial pre-industrial climate, as well as uncertainties in the representation of land-atmosphere CO₂ exchange. Changing from a Q ₁₀ form for plant dark and maintanence respiration (as used in the coupled GCM runs) to a respiration proportional to maximum photosynthesis, reduces the biomass lost from Amazonia in the 21st century. Replacing the GCM control climate (which has about 25% too little rain in the annual mean over Amazonia) with an observed climatology increases the CO₂ concentration at which rainfall drops to critical levels, and thereby further delays the dieback. On the other hand, calibration of the canopy photosynthesis model against Amazonian flux data tends to lead to earlier forest dieback. Further advances are required in both GCM rainfall simulation and land-surface process representation before a clearer picture will emerge on the timing of possible Amazonian forest dieback. However, it seems likely that these advances will overall lead to projections of later forest dieback as GCM control climates become more realistic.     

Possible climatic impacts of tropical deforestation

Large-scale conversion of tropical forests into pastures or annual crops will likely lead to changes in the local microclimate of those regions. Larger diurnal fluctuations of surface temperature and humidity deficit, increased surface runoff during rainy periods and decreased runoff during the dry season, and decreased soil moistrue are to be expected.It is likely that evapotranspiration will be reduced because of less available radiative energy at the canopy level since grass presents a higher albedo than forests, also because of the reduced availability of soil moisture at the rooting zone primarily during the dry season. Recent results from general circulation model (GCM) simulations of Amazonian deforestation seem to suggest that the equilibrium climate for a grassy vegetation in Amazonia would be one in which regional precipitation would be significantly reduced.Global climate changes probably will occur if there is a marked change in rainfall patterns in tropical forest regions as a result of deforestation. Besides that, biomass burning of tropical forests is likely adding CO₂ into the atmosphere, thus contributing to the enhanced greenhouse warming.     

Projections of Extreme Rainfall in Hong Kong in the 21st Century

The possible changes in the frequency of extreme rainfall events in Hong Kong in the 21st century wereinvestigated by statistically downscaling 30 sets of the daily global climate model projections (involvinga combination of 12 models and 3 greenhouse gas emission scenarios,namely,A2,A1B,and B1) of theFourth Assessment Report of the Intergovernmental Panel on Climate Change.To cater for the intermittentand skewed character of the daily rainfall,multiple stepwise logistic regression and multiple stepwise linearregression were employed to develop the downscaling models for predicting rainfall occurrence and rainfallamount,respectively.Verification of the simulation of the 1971-2000 climate reveals that the models ingeneral have an acceptable skill in reproducing past statistics of extreme rainfall events in Hong Kong.Theprojection results suggest that,in the 21st century,the annual number of rain days in Hong Kong is expectedto decrease while the daily rainfall intensity will increase,concurrent with the expected increase in annualrainfall.Based on the multi-model scenario ensemble mean,the annual number of rain day is expected todrop from 104 days in 1980-1999 to about 77 days in 2090-2099.For extreme rainfall events,about 90% ofthe model-scenario combinations indicate an increase in the annual number of days with daily rainfall 100mm (R100) towards the end of the 21st century.The mean number of R100 is expected to increase from 3.5days in 1980-1999 to about 5.3 days in 2090-2099.The projected changes in other extreme rainfall indicesalso suggest that the rainfall in Hong Kong in the 21st century may also become more extreme with moreuneven distributions of wet and dry periods.While most of the model-emission scenarios in general projectconsistent trends in the change of rainfall extremes in the 21st century,there is a large divergence in theprojections among different model/emission scenarios.This reflects that there are still large uncertainties inmodel simulations of future extreme rainfall events.