REGIONAL CLIMATE CHANGE SCENARIOS UNDER GLOBAL WARMING IN KAZAKHSTAN

The aim of this paper is to report on the development of regional climate change scenarios for Kazakhstan as the result of increasing of CO₂ concentration in the global atmosphere. These scenarios are used in the assessment of climate change impacts on the agricultural, forest and water resources of Kazakhstan. Climate change scenarios for Kazakhstan to assess both long-term (2× CO₂ in 2075) and short-term (2000, 2010 and 2030) impacts were prepared. The climate conditions under increasing CO₂ concentration were estimated from three General Circulation Models (GCM) outputs: the model of the Canadian Climate Center Model (CCCM), the model of the Geophysical Fluid Dynamics Laboratory (GFDL) and the 1% transient version of the GFDL model (GFDL-T). The near-term climate scenarios were obtained using the probabilistic forecast model (PFM) to the year 2010 and the results of GFDL-T for years 2000 and 2030. A baseline scenario representing the current climate conditions based on observations from 1951 to 1980 was developed. The assessment of climate change in Kazakhstan based on the analysis of 100-years observations is given too. As a result of comparisons of the current climate (based on observed climate) the 1× CO₂ output from GCMs showed that the GFDL model best matches the observed climate. The GFDL model suggests that the minimum increase in temperature is expected in winter, when most of the territory is expected to have temperatures 2.3–4.5 °C higher. The maximum (4.3 to 8.2 °C) is expected to be in spring. CCCM scenario estimates an extreme worming above 11 °C in spring months. GFDL-T outputs provide an intermediate scenario.     

The potential effects of climate change on summer season dairy cattle milk production and reproduction

The potential direct effects of possible global warming on summer season dairy production and reproduction were evaluated for the United States and Europe. Algorithms used for milk production and conception rate were previously developed and validated. Three widely known global circulation models (GISS, GFDL, and UKMO) were used to represent possible scenarios of future climate. Milk production and conception rate declines were highest under the UKMO model scenario and lowest under the GISS model scenario. Predicted declines for the GCM scenarios are generally higher than either 1 year in 10 probability-based declines or declines based on the abnormally hot summer of 1980 in the United States. The greatest declines (about 10% for the GISS and GFDL scenarios, and about 20% for the UKMO scenario) in the United States are predicted to occur in the Southeast and the Southwest. Substantial declines (up to 35%) in conception rates were also predicted in many locations, particularly the eastern and southern United States. These areas correspond to areas of high dairy cattle concentration. They already have relatively large summer season milk production declines resulting from normally hot conditions. Thus, the actual impacts of increased production declines may be greater in other areas, which are not accustomed to large summer season declines and therefore may require more extensive mitigation measures.Published as Paper No. 9698 Journal Series, Nebraska Agricultural Research Division. The work reported here was conducted under Nebraska Agricultural Research Division Project 27–007.     

The effect of changing climate on Australian biomass production — a preliminary study

Increasing concentrations of carbon dioxide and other infrared absorbing gases are widely proposed as a mechanism for a global surface warming over the next several decades. Various methods have been used to anticipate the regional climatic changes which might result. In earlier papers Pittock has concluded that for Australia an increase in the extent of the summer rainfall regime is likely, with a decrease in winter rainfall. This is similar to a change which took place over much of Australia in the 1940's.In this paper a climate scenario, roughly corresponding to the climate which might be expected to occur by about the middle of the twenty-first century, has been used as the input into the Miami Model of net primary productivity due to Lieth. Results show that about half of Australia might experience productivity increases in excess of 20%. A small area in the extreme south-west of Australia shows a small decrease in productivity.The direct response of plants to higher ambient carbon dioxide concentrations, and many other possible effects such as changes in the incidence of plant diseases, pests, and soil erosion have not been assessed.Whether or not increasing carbon dioxide will lead to further climatic change in the next few decades, the results show that Australian primary productivity is quite sensitive to climatic variations within the range found in the historical records.     

Influences on surface energy fluxes in simulated present and doubled CO2 climates

The surface energy fluxes simulated by the CSIRO9 Mark 1 GCM for present and doubled CO₂ conditions are analyzed. On the global scale the climatological flux fields are similar to those from four GCMs studied previously. A diagnostic calculation is used to provide estimates of the radiative forcing by the GCM atmosphere. For 1 × CO₂, in the global and annual mean, cloud produces a net cooling at the surface of 31 W m^–2. The clear-sky longwave surface greenhouse effect is 311 W m^–2, while the corresponding shortwave term is –79 W m^–2. As for the other GCM results, the CSIRO9 CO₂ surface warming (global mean 4.8°C) is closely related to the increased downward longwave radiation (LW ). Global mean net cloud forcing changes little. The contrast in warming between land and ocean, largely due to the increase in evaporative cooling (E) over ocean, is highlighted. In order to further the understanding of influences on the fluxes, simple physically based linear models are developed using multiple regression. Applied to both 1 × CO₂ and CO₂ December–February mean tropical fields from CSIRO9, the linear models quite accurately (3–5 W m^–2 for 1 × CO₂ and 2–3 W m^–2 for CO₂) relate LW and net shortwave radiation to temperature, surface albedo, the water vapor column, and cloud. The linear models provide alternative estimates of radiative forcing terms to those from the diagnostic calculation. Tropical mean cloud forcings are compared. Over land, E is well correlated with soil moisture, and sensible heat with air-surface temperature difference. However an attempt to relate the spatial variation of LWt within the tropics to that of the nonflux fields had little success. Regional changes in surface temperature are not linearly related to, for instance, changes in cloud or soil moisture.     

Paleoclimate data constraints on climate sensitivity: The paleocalibration method

The relationship between paleoclimates and the future climate, while not as simple as implied in the paleoanalog studies of Budyko and others, nevertheless provides sufficient constraints to broadly confirm the climate sensitivity range of theoretical models and perhaps eventually narrow the model-derived uncertainties. We use a new technique called paleocalibration to calculate the ratio of temperature response to forcing on a global mean scale for three key intervals of Earth history. By examining surface conditions reconstructed from geologic data for the Last Glacial Maximum, the middle Cretaceous and the early Eocene, we can estimate the equilibrium climate sensitivity to radiative forcing changes for different extreme climates. We find that the ratios for these three periods, within error bounds, all lie in the range obtained from general circulation models: 2–5 K global warming for doubled atmospheric carbon dioxide. Paleocalibration thus provides a data-based confirmation of theoretically calculated climate sensitivity. However, when compared with paleodata on regional scales, the models show less agreeement with data. For example, our GCM simulation of the early Eocene fails to obtain the temperature contrasts between the Equator and the Poles (and between land and ocean areas) indicated by the data, even though it agrees with the temperature data in the global average. Similar results have been reported by others for the Cretaceous and for the Last Glacial Maximum.The U.S. Government right to retain a non-exclusive royalty-free license in and to any copyright is acknowledged.     

Increasing cloud in a warming world

Cloud amount records for the U.S.A. have been analyzed in the context of the warming world analogue model described by Lough et al. (1983). Cloud amount increases over practically the entire U.S.A. in all seasons. This result considerably strengthens the more tentative conclusion of Henderson-Sellers (1986) that cloud amount increases over Europe in the same warming world scenario. These results are in contrast to the few numerical model predictions of cloud changes in warming world experiments. A possible, rather tantalizing, conclusion is that current GCM cloud prediction schemes tend to enhance temperature increases through cloud-climate feedback whereas the historical data could suggest a negative feedback. Part, possibly all, of this difference may be the result of the fundamental distinction between the two experimental scenarios: the equilibrium change modelled by GCMs as compared to the smaller transient change represented by the historical analogue. On the other hand the current real-world experiment is, itself, a transient change in boundary and atmospheric conditions. At the least, surface-observed cloudiness seems to offer a useful and complementary data source with which to examine one aspect of the performance of numerical climate models.     

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.     

Estimation of impact of climate change on the peak discharge probability of the river Rhine

RHINEFLOW is a GIS based water balance model that has been developed to study the changes in the water balance compartments of the river Rhine basin on a monthly time basis. The model has been designed to study the sensitivity of the Rhine discharge to a climate change. The calculated discharge has been calibrated and validated on the period 1956 to 1980. For this period the model efficiency of RHINEFLOW is between 0.74 and 0.81 both for the entire Rhine and for its tributaries. Also calculated values for variations in other compartments, e.g. snow storage and actual evapotranspiration, were in good agreement with the measured values.Since a high correlation between monthly discharge and peak discharge was found for the period 1900–1980 The RHINEFLOW model is used to assess the probability of exceedence for discharge peaks under possible future climate conditions.The probabilities of exceedence were calculated from the conditional probabilities of peak discharges for a series of 15 classes of monthly discharges. Comparison of a calculated frequency distribution of high discharge peaks with observed peaks in a test series showed that the method performs well.Scenarios for temperature changes between 0 °C and plus 4 °C and precipitation changes between plus 20% and minus 20% have been applied. Within this range flood frequencies are more sensitive for a precipitation change than for a temperature change. The present two-year return period peak flow (6500–7000 m³/s) decreases by about 6% due to a temperature rise of 4 °C; a precipitation decrease of 20% leads to 30% lower two-year peaks whilst 20% precipitation increase raises them by approximately 30%.Application of a Business As Usual (BAU) and an Accelerated Policy (AP) climate scenario resulted in a significant increase in probability of peak flows for the BAU scenario, while for the AP scenario no significant change could be found. Due to sampling errors, accurate estimations of recurrence times of discharge peaks7000 m³/s require a longer sampling time series than 90 years. For management purposes the method can be applied to estimate changes of probabilities of events with a relatively long recurrence time.     

Global comparison of the regional rainfall results of enhanced greenhouse coupled and mixed layer ocean experiments: Implications for climate change scenario development

The extent of agreement amongst current global climate models (GCMs) on the global pattern of rainfall change simulated under enhanced greenhouse conditions is assessed. We consider the results of five experiments which use a simple mixed layer ocean formulation and five which use a fully dynamic ocean model (coupled experiments). For many regions of the northern hemisphere there is strong agreement amongst both mixed layer and coupled experiments on the sign of simulated rainfall change. However, in the southern hemisphere there are large, and apparently systematic, differences between the coupled and mixed layer experiments. In particular, whereas the mixed layer experiments agree on simulated rainfall increase in summer in the tropics and subtropics of the Australian sector, the coupled experiments agree (although more weakly) on rainfall decreases. These differences appear to relate to the much reduced warming simulated by the coupled experiments in the high latitudes of the southern hemisphere. However, recent oceanographie evidence suggests that this suppressed warming may be considerably overestimated. We conclude therefore that despite the in-principle advantages of coupled models, it may be too soon to base some regionally specific climate change scenarios solely on the results of coupled experiments.     

Amazonian forest dieback under climate-carbon cycle projections for the 21st century

Summary The first GCM climate change projections to include dynamic vegetation and an interactive carbon cycle produced a very significant amplification of global warming over the 21st century. Under the IS92a business as usual emissions scenario CO₂ concentrations reached about 980ppmv by 2100, which is about 280ppmv higher than when these feedbacks were ignored. The major contribution to the increased CO₂ arose from reductions in soil carbon because global warming is assumed to accelerate respiration. However, there was also a lesser contribution from an alarming loss of the Amazonian rainforest. This paper describes the phenomenon of Amazonian forest dieback under elevated CO₂ in the Hadley Centre climate-carbon cycle model.     

Climate change and snow-cover duration in the Australian Alps

This study uses a model of snow-cover duration, an observed climate data set for the Australian alpine area, and a set of regional climate-change scenarios to assess quantitatively how changes in climate may affect snow cover in the Australian Alps. To begin, a regional interannual climate data set of high spatial resolution is prepared for input to the snow model and the resulting simulated interannual and spatial variations in snow-cover duration are assessed and compared with observations. The model provides a reasonable simulation of the sensitivities of snow-cover duration to changes in temperature and precipitation in the Australian Alps, although its performance is poorer at sites highly marginal for snow cover. (In a separate comparison, the model also performs well for sites in the European Alps.) The input climate data are then modified in line with scenarios of regional climate change based on the results of five global climate models run in enhanced greenhouse experiments. The scenarios are for the years 2030 and 2070 and allow for uncertainty associated with projecting future emissions of greenhouse gases and with estimating the sensitivity of the global climate system to enhanced greenhouse forcing. Attention focuses on the climate changes most favourable (best-case scenario) and least favourable (worst-case scenario) for snow cover amongst the range of climate changes in the scenarios. Under the best case scenario for 2030, simulated average snow-cover duration and the frequency of years of more than 60 days cover decline at all sites considered. However, at the higher sites (e.g., more than 1700 m) the effect is not very marked. For the worst case scenario, a much more dramatic decline in snow conditions is simulated. At higher sites, simulated average snow cover duration roughly halves by 2030 and approaches zero by 2070. At lower sites (around 1400 m), near zero average values are simulated by 2030 (compared to durations of around 60 days for current climate).These simulated changes, ranging between the best and worst case, are likely to be indicative of how climate change will affect natural snow-cover duration in the Australian Alps. However, note that the model does not allow directly for changes in the frequency and intensity of snow-bearing circulation systems, nor do the climate-change scenarios allow possible changes in interannual variability (particularly that due to the El Niño-Southern Oscillation) and local topographical effects not resolved by global climate models. The simulated changes in snow cover are worthy of further consideration in terms of their implications for the ski industry and tourism, water resources and hydroelectric power, and land-use management and planning.68 Barada Crescent, Aranda ACT 2614, Australia.     

The risk of sea level rise

The United Nations Framework Convention on Climate Change requires nations to implement measures for adapting to rising sea level and other effects of changing climate. To decide upon an appropriate response, coastal planners and engineers must weigh the cost of these measures against the likely cost of failing to prepare, which depends on the probability of the sea rising a particular amount.This study estimates such a probability distribution, using models employed by previous assessments, as well as the subjective assessments of twenty climate and glaciology reviewers about the values of particular model coefficients. The reviewer assumptions imply a 50 percent chance that the average global temperature will rise 2 °C, as well as a 5 percent chance that temperatures will rise 4.7 °C by 2100. The resulting impact of climate change on sea level has a 50 percent chance of exceeding 34 cm and a 1% chance of exceeding one meter by the year 2100, as well as a 3 percent chance of a 2 meter rise and a 1 percent chance of a 4 meter rise by the year 2200.The models and assumptions employed by this study suggest that greenhouse gases have contributed 0.5 mm/yr to sea level over the last century. Tidal gauges suggest that sea level is rising about 1.8 mm/yr worldwide, and 2.5–3.0 mm/yr along most of the U.S. Coast. It is reasonable to expect that sea level in most locations will continue to rise more rapidly than the contribution from climate change alone.We provide a set of normalized projections which express the extent to which climate change is likely to accelerate the rate of sea level rise. Those projections suggest that there is a 65 percent chance that sea level will rise 1 mm/yr more rapidly in the next 30 years than it has been rising in the last century. Assuming that nonclimatic factors do not change, there is a 50 percent chance that global sea level will rise 45 cm, and a 1 percent chance of a 112 cm rise by the year 2100; the corresponding estimates for New York City are 55 and 122 cm.Climate change impact assessments concerning agriculture, forests, water resources, and other noncoastal resources should also employ probability-based projections of regional climate change. Results from general circulation models usually provide neither the most likely scenario nor the full range of possible outcomes; probabilistic projections do convey this information. Moreover, probabilistic projections can make use of all the available knowledge, including the views of skeptics; the opinions of those who study ice cores, fossils, and other empirical evidence; and the insights of climate modelers, which may be as useful as the model results themselves.The U.S. Government right to retain a non-exclusive royalty-free license in and to any copyright is acknowledged.     

Lake ice records used to detect historical and future climatic changes

Historical ice records, such as freeze and breakup dates and the total duration of ice cover, can be used as a quantitative indicator of climatic change if long homogeneous records exist and if the records can be calibrated in terms of climatic changes. Lake Mendota, Wisconsin, has the longest uninterrupted ice records available for any lake in North America dating back to 1855. These records extend back prior to any reliable air temperature data in the midwestern region of the U.S. and demonstrate significant warming of approximately 1.5 °C in fall and early winter temperatures and 2.5 °C in winter and spring temperatures during the past 135 years. These changes are not completely monotonie, but rather appear as two shorter periods of climatic change in the longer record. The first change was between 1875 and 1890, when fall, winter, and spring air temperatures increased by approximately 1.5 °C. The second change, earlier ice breakup dates since 1979, was caused by a significant increase in winter and early spring air temperatures of approximately 1.3 °C. This change may be indicative of shifts in regional climatic patterns associated with global warming, possibly associated with the Greenhouse Effect.With the relationships between air temperature and freeze and break up dates, we can project how the ice cover of Lake Mendota should respond to future climatic changes. If warming occurs, the ice cover for Lake Mendota should decrease approximately 11 days per 1 °C increase. With a warming of 4 to 5 °C, years with no ice cover should occur in approximately 1 out of 15 to 30 years.     

Vulnerability of Island Tropical Montane Cloud Forests to Climate Change, with Special Reference to East Maui, Hawaii

Island tropical montane cloud forests may be among the most sensitive of the world's ecosystems to global climate change. Measurements in and above a montane cloud forest on East Maui, Hawaii, document steep microclimatic gradients. Relatively small climate-driven shifts in patterns of atmospheric circulation are likely to trigger major local changes in rainfall, cloud cover, and humidity. Increased interannual variability in precipitation and hurricane incidence would provide additional stresses on island biota that are highly vulnerable to disturbance-related invasion of non-native species. Because of the exceptional sensitivity of these microclimates and forests to change, they may provide valuable listening posts for detecting the onset of human-induced global climate change.     

Solar Forcing of Global Temperature Change Since AD 1400

It is well known that the magnetic field imbedded in the solar wind modulates the production of cosmogenic isotopes by galactic cosmic rays. Power spectral analysis yields evidence for fundamental periods relevant to this study including the Suess, Gleissberg, Hale and Schwabe cycles of ca. 210, 88, 22 and 11 years lengths. There is increasing evidence for an irradiance component accompanying each of these cycles. Assuming this is valid, we model the magnitude of irradiance change associated with these cycles that is compatible with the paleoclimate record. We conclude that the resultant model fit requires less than ±0.8 change in solar irradiance for each of these cycles even if we assume low climate sensitivity (0.5 °C(Wm^–2)). Our solar irradiance model accounts for about 18% of 20th century global warming to 1997 and also predicts that the next maximum would occur in ad 2040 and contribute 0.2 °C to 21st century Northern Hemisphere warming.     

Nested regional simulation of climate change over the Alps for the scenario of a doubled greenhouse forcing

Summary Simulated temperature and precipitation changes over western Europe for a scenario of doubled atmospheric concentrations of CO₂ are presented. The simulations are performed using a Limited Area Model LAM (RegCM2) nested into a General Circulation Model (ECHAM3). Both model components are operated at very high spatial resolutions — approximately 120 km for the GCM and 20 km for the LAM; the LAM domain encompasses a region of 1100 × 1100 km squared. Climatologies for five January and five July periods have been simulated. Average surface (2 m) temperatures are found to increase by 1.4 K in winter (January) and 3.9 K in summer (July); this latter figure is, however, largely dependent on a positive bias in the summer temperature fields of the driving GCM. Average precipitation changes are generally small in absolute values, but exhibit considerable spatial variability. Large precipitation amounts are seen to be shifted towards higher elevations with a corresponding reduction in the upwind areas. The results are discussed taking into account the predictive skill of the modelling system, which is derived from comparing the simulated present day temperature and precipitation fields to the corresponding climatological information. A method is introduced to assess the reliability of climate scenario predictions — such as those discussed here — on the basis of this predictive skill.With 14 Figures     

The implications of climate policy for the impacts of climate change on global water resources

This paper assesses the implications of climate policy for exposure to water resources stresses. It compares a Reference scenario which leads to an increase in global mean temperature of 4 °C by the end of the 21st century with a Mitigation scenario which stabilises greenhouse gas concentrations at around 450 ppm CO₂e and leads to a 2 °C increase in 2100. Associated changes in river runoff are simulated using a global hydrological model, for four spatial patterns of change in temperature and rainfall. There is a considerable difference in hydrological change between these four patterns, but the percentages of change avoided at the global scale are relatively robust. By the 2050s, the Mitigation scenario typically avoids between 16 and 30% of the change in runoff under the Reference scenario, and by 2100 it avoids between 43 and 65%. Two different measures of exposure to water resources stress are calculated, based on resources per capita and the ratio of withdrawals to resources. Using the first measure, the Mitigation scenario avoids 8-17% of the impact in 2050 and 20-31% in 2100; with the second measure, the avoided impacts are 5-21% and 15-47% respectively. However, at the same time, the Mitigation scenario also reduces the positive impacts of climate change on water scarcity in other areas. The absolute numbers and locations of people affected by climate change and climate policy vary considerably between the four climate model patterns.     

Variation of Ecosystems over East Asia in Association with Seasonal, Interannual and Decadal Monsoon Climate Variability

Nearly half of the global low latitudes are characterized by a monsoon climate. This paper first analyzes the global spatial distribution of the rate of climate variation based on precipitation data. Results show that the monsoon regions in Asia and West Africa, and to a lesser extent in Australia, have the highest rate of climate variation on all time scales. These variations are manifested as seasonal jumps, high interannual and interdecadal variabilities, and abrupt changes between climate regimes. The monsoon regions are covered by various types of ecosystems which account for a large portion of the global biomass. Further analyses of the variations of ecosystems in the Asian region and their relationships with the monsoon climate have shown that the spatial and temporal variabilities of ecosystems are characterized by their strong response to variations in monsoon rainfall, one of the major energy flows in terrestrial ecosystems. The high rate of variation in monsoon climate strongly influences variation in Asian ecosystems. Changes in Asian ecosystems seem to be mainly driven by variations in monsoon climate over various time scales. This observation has led to the proposal of monsoon-driven ecosystems in Asia.     

A climate change simulation starting from 1935

Due to restrictions in the available computing resources and a lack of suitable observational data, transient climate change experiments with global coupled ocean-atmosphere models have been started from an initial state at equilibrium with the present day forcing. The historical development of greenhouse gas forcing from the onset of industrialization until the present has therefore been neglected. Studies with simplified models have shown that this cold start error leads to a serious underestimation of the anthropogenic global warming. In the present study, a 150-year integration has been carried out with a global coupled ocean-atmosphere model starting from the greenhouse gas concentration observed in 1935, i.e., at an early time of industrialization. The model was forced with observed greenhouse gas concentrations up to 1985, and with the equivalent C0₂ concentrations stipulated in Scenario A (Business as Usual) of the Intergovernmental Panel on Climate Change from 1985 to 2085. The early starting date alleviates some of the cold start problems. The global mean near surface temperature change in 2085 is about 0.3 K (ca. 10%) higher in the early industrialization experiment than in an integration with the same model and identical Scenario A greenhouse gas forcing, but with a start date in 1985. Comparisons between the experiments with early and late start dates show considerable differences in the amplitude of the regional climate change patterns, particularly for sea level. The early industrialization experiment can be used to obtain a first estimate of the detection time for a greenhouse-gas-induced near-surface temperature signal. Detection time estimates are obtained using globally and zonally averaged data from the experiment and a long control run, as well as principal component time series describing the evolution of the dominant signal and noise modes. The latter approach yields the earliest detection time (in the decade 1990–2000) for the time-evolving near-surface temperature signal. For global-mean temperatures or for temperatures averaged between 45°N and 45°S, the signal detection times are in the decades 2015–2025 and 2005–2015, respectively. The reduction of the cold start error in the early industrialization experiment makes it possible to separate the near-surface temperature signal from the noise about one decade earlier than in the experiment starting in 1985. We stress that these detection times are only valid in the context of the coupled model's internally-generated natural variability, which possibly underestimates low frequency fluctuations and does not incorporate the variance associated with changes in external forcing factors, such as anthropogenic sulfate aerosols, solar variability or volcanic dust.     

On the Response of the Greenland Ice Sheet to Greenhouse Climate Change

Numerical computations are performed with the three-dimensional polythermal ice-sheet model SICOPOLIS in order to investigate the possible impact of a greenhouse-gas-induced climate change on the Greenland ice sheet. The assumed increase of the mean annual air temperature above the ice covers a range from T = 1°C to 12°C, and several parameterizations for the snowfall and the surface melting are considered. The simulated shrinking of the ice sheet is a smooth function of the temperature rise, indications for the existence of critical thresholds of the climate input are not found. Within 1000 model years, the ice-volume decrease is limited to 10% of the present volume for T 3°C, whereas the most extreme scenario, T = 12°C, leads to an almost entire disintegration, which corresponds to a sea-level equivalent of 7 m. The different snowfall and melting parameterizations yield an uncertainty range of up to 20% of the present ice volume after 1000 model years.