COMPARISON OF GLOBAL CLIMATE CHANGE SIMULATIONS FOR 2 × CO2-INDUCED WARMING

In this article we compile 108 national and international studies on global climate change, each projecting a quantitative impact on global surface-air temperature due to a doubling of the atmospheric CO₂ concentration. These predictions, documented between 1980 and 1995, are based primarily on climate-modeling research, including radiative-convective, energy-balance, and general circulation models. Collectively over the past 15 years, the average (mean) temperature change projection due to doubled CO₂ is +2.62°C, with a range of 0.16–8.7°C. General circulation models tend to estimate slightly higher values (2.98°C), compared with radiative-convective models (1.98°C) and energy-balance models (2.54°C). During the years 1980 through 1995, an increasing trend in predictions is noticed, although the mean temperature change prediction each year has remained fairly consistent near 2–3°C. These findings suggest that the estimated sensitivity of the climate system continues to remain comparable to the range calculated in earlier studies. However, tremendous advancements in the capacity of climate models continue to reveal important uncertainties in the dynamic nature of global atmospheric interactions. The predictions continue to validate the need for a global policy relating to human influence on global climate change.     

Signals of climatic change in the Central-Western Mediterranean basin

Summary Climatic scenario models forecast an increase of the air temperature in the next century of 1.5–3.5 °C, because of the anthropogenic enhancement of the concentration greenhouse gases in the atmosphere. The analysis of the trend of long-lasting data series of climatic parameters seems to support such a prediction: indeed due to the increase of greenhouse gases in the atmosphere, a climate modification could be already ongoing. Several papers have been published dealing with the global scale climate, this paper, however, deals with an investigation on the regional scale, referring specifically to the Central-Western Mediterranean basin. We are concerned with the parameters which are more affected by climate changes, such as pressure, temperature and precipitation. The analysis carried out indicates that in the Central-Western Mediterranean basin the climate is evolving in a consistent way; we have found: i) an increase of air pressure at the surface and at the upper levels; ii) a reduction in cloudiness and precipitation amount; iii) an increase by about 1 °C in surface air temperature during the period 1860–1995 and in more recent years a rise of the freezing level and of the tropopause; iv) a reduction of strong cyclogenetic events and an increase of heat waves. These results, although compatible with the scenarios predicted, do not allow a final conclusion to be drawn concerning a man-made influence on climate change in the basin.With 13 Figures     

A Bayesian Statistical Analysis of the Enhanced Greenhouse Effect

This paper demonstrates that there is a robust statistical relationship between the records of the global mean surface air temperature and the atmospheric concentration of carbon dioxide over the period 1870–1991. As such, the enhanced greenhouse effect is a plausible explanation for the observed global warming. Long term natural variability is another prime candidate for explaining the temperature rise of the last century. Analysis of natural variability from paleo-reconstructions, however, shows that human activity is so much more likely an explanation that the earlier conclusion is not refuted. But, even if one believes in large natural climatic variability, the odds are invariably in favour of the enhanced greenhouse effect. The above conclusions hold for a range of statistical models, including one that is capable of describing the stabilization of the global mean temperature from the 1940s to the 1970s onwards. This model is also shown to be otherwise statistically adequate. The estimated climate sensitivity is about 3.8 °C with a standard deviation of 0.9 °C, but depends slightly on which model is preferred and how much natural variability is allowed. These estimates neglect, however, the fact that carbon dioxide is but one of a number of greenhouse gases and that sulphate aerosols may well have dampened warming. Acknowledging the fact that carbon dioxide is used as a proxy for all human induced changes in radiative forcing brings a lot of additional uncertainty. Prior knowledge on both climate sensitivity and radiative forcing is needed to say anything about the respective sizes. A fully Bayesian approach is used to combine expert knowledge with information from the observations. Prior knowledge on the climate sensitivity plays a dominant role. The data largely exclude climate sensitivity to be small, but cannot exclude climate sensitivity to be large, because of the possibility of strong negative sulphate forcing. The posterior of climate sensitivity has a strong positive skewness. Moreover, its mode (again 3.8 °C; standard deviation 2.4 °C) is higher than the best guess of the IPCC.     

The use of scenarios as the basis for combined assessment of climate change mitigation and adaptation

Scenarios are used to explore the consequences of different adaptation and mitigation strategies under uncertainty. In this paper, two scenarios are used to explore developments with (1) no mitigation leading to an increase of global mean temperature of 4 °C by 2100 and (2) an ambitious mitigation strategy leading to 2 °C increase by 2100. For the second scenario, uncertainties in the climate system imply that a global mean temperature increase of 3 °C or more cannot be ruled out. Our analysis shows that, in many cases, adaptation and mitigation are not trade-offs but supplements. For example, the number of people exposed to increased water resource stress due to climate change can be substantially reduced in the mitigation scenario, but adaptation will still be required for the remaining large numbers of people exposed to increased stress. Another example is sea level rise, for which, from a global and purely monetary perspective, adaptation (up to 2100) seems more effective than mitigation. From the perspective of poorer and small island countries, however, stringent mitigation is necessary to keep risks at manageable levels. For agriculture, only a scenario based on a combination of adaptation and mitigation is able to avoid serious climate change impacts.     

Recent climatic change,greenhouse gas emissions and future climate: The implications for India

Summary In this paper, we discuss past climatic trends over India, greenhouse gas emissions due to energy consumption, forest and land-use changes, climate change scenarios for the year 2050, potential consequences for agriculture and cyclone activity and the possibility that India might limit the increasing trend in its emissions.India's mean surface air temperature has increased significantly by about 0.4°C over the past ccntury. Neither monsoon nor annual rainfall shows any significant trend. On average, there has been a rise in sea levels around India over recent decades, though considerable uncertainties exist in the accuracy and interpretation of the available data.Carbon emissions from the energy sector amount to 71 MT a year, equivalent to all other sectors combined. From land-use data, a marginal net sequestration of 5.25 million tonnes of carbon occurred during 1986. Following the IPCC guidelines, methane emissions from rice and livestock are estimated at 17.4 and 12.8 Tg/year, respectively.According to recent climate model projections, India may experience a further rise in temperature of 1 °C by the year 2050, about four times the rate of warming experienced over the past 100 years. A modest increase in precipitation amounts might occur. Cereals production is estimated to decrease and the nutrition security of the population-rich but land-hungry region of India might be hampered. An increase in local tropical cyclone activity may occur over thc next century, posing added problems as large areas in the coastal regions have a dense population.About 70% of the electricity generation in India is from coal-based power stations. Altering this dependence significantly to reduce emissions would imply a substantial change in the present energy policy of India. There is great potential for improving energy efficiency and conservation. The adoption of cleaner coal-technologies should be considered, as must the development of renewable, non-conventional energy sources. In all cases, serious institulional barriers and resource limitations need to be addressed. The scope for carbon sequestration is limiled by land availabilily and other factors. It is argued that any response to global warming must be located firmly in the framework of sustainable development.With 5 Figures     

Projected global climate change impact on water temperatures in five north central U.S. streams

The effect of projected global climate change due to a doubling of atmospheric CO₂ on water temperatures in five streams in Minnesota was estimated using a deterministic heat transport model. The model calculates heat exchange between the atmosphere and the water and is driven by climate parameters and stream hydrologic parameters. The model is most sensitive to air temperature and solar radiation. The model was calibrated against detailed measurements to account for seasonally variable shading and wind sheltering. Using climate projections from the GISS, GFDL and OSU GCMs as input; stream temperature simulations predict a warming of freely flowing river reaches by 2.4 °C to 4.7 °C when atmospheric CO₂ doubles. In small shaded streams water temperatures are predicted to rise by an additional 6 °C in summer if trees along stream banks should be lost due to climate change or other human activities (e.g. logging). These projected water temperature changes have significant consequences for survival and growth of fishes. Simulation with the complete heat budget equations were also used to examine simplified water temperature/air temperature correlations.     

Natural and anthropogenic climate change: incorporating historical land cover change,vegetation dynamics and the global carbon cycle

This study explores natural and anthropogenic influences on the climate system, with an emphasis on the biogeophysical and biogeochemical effects of historical land cover change. The biogeophysical effect of land cover change is first subjected to a detailed sensitivity analysis in the context of the UVic Earth System Climate Model, a global climate model of intermediate complexity. Results show a global cooling in the range of –0.06 to –0.22 °C, though this effect is not found to be detectable in observed temperature trends. We then include the effects of natural forcings (volcanic aerosols, solar insolation variability and orbital changes) and other anthropogenic forcings (greenhouse gases and sulfate aerosols). Transient model runs from the year 1700 to 2000 are presented for each forcing individually as well as for combinations of forcings. We find that the UVic Model reproduces well the global temperature data when all forcings are included. These transient experiments are repeated using a dynamic vegetation model coupled interactively to the UVic Model. We find that dynamic vegetation acts as a positive feedback in the climate system for both the all-forcings and land cover change only model runs. Finally, the biogeochemical effect of land cover change is explored using a dynamically coupled inorganic ocean and terrestrial carbon cycle model. The carbon emissions from land cover change are found to enhance global temperatures by an amount that exceeds the biogeophysical cooling. The net effect of historical land cover change over this period is to increase global temperature by 0.15 °C.     

Predicting Patterns of Near-Surface Air Temperature Using Empirical Data

The signal of recent global warming has been detected in meteorological records, borehole temperatures and by several indirect climate indicators. Anthropogenic warming continues to evolve, and various methods are used to study and predict the changes of the global and regional climate. Results derived from GCMs, palaeoclimate reconstructions, and regional climate models differ in detail. An empirical model could be used to predict the spatial pattern of the near-surface air temperature and to narrow the range of regional uncertainties. The idea behind this approach is to study the correlations between regional and global temperature using century-scale meteorological records, and to evaluate the regional pattern of the future climate using regression analysis and the global-mean air temperature as a predictor. This empirical model, however, is only applicable to those parts of the world where regional near-surface air temperature reacts linearly to changes of the global thermal regime. This method and data from a set of approximately 2000 weather stations with continuous century-scale records of the monthly air temperature was applied to develop the empirical map of the regional climate sensitivity. Data analysis indicated that an empirical model could be applied to several large regions of the World, where correlations between local and global air temperature are statistically significant. These regions are the western United States, southern Canada, Alaska, Siberia, south-eastern Asia, southern Africa and Australia, where the correlation coefficient is typically above 0.9. The map of regional climate sensitivity has been constructed using calculated coefficients of linear regression between the global-mean and regional annual air temperature. As long as the correlations between the local and global air temperature are close to those in the last several decades, this map provides an effective tool to scale down the projection of the global air temperature to regional level. According to the results of this study, maximum warming at the beginning of the 21st century will take place in the continental parts of North America and Eurasia. The empirical regional climate sensitivity defined here as the response of the mean-annual regional temperature to 1 °C global warming was found to be 5–6 °C in southern Alaska, central Canada, and over the continental Siberia, 3–4 °C on the North Slope of Alaska and western coast of the U.S.A., and 1–2 °C in most of the central and eastern U.S.A. and eastern Canada. Regions with negative sensitivity are located in the southeastern U.S.A., north-western Europe and Scandinavia. The local tendency towards cooling, although statistically confirmed by modern data, could, however, change in the near future.     

Multi-fingerprint detection and attribution analysis of greenhouse gas, greenhouse gas-plus-aerosol and solar forced climate change

 A multi-fingerprint analysis is applied to the detection and attribution of anthropogenic climate change. While a single fingerprint is optimal for the detection of climate change, further tests of the statistical consistency of the detected climate change signal with model predictions for different candidate forcing mechanisms require the simultaneous application of several fingerprints. Model-predicted climate change signals are derived from three anthropogenic global warming simulations for the period 1880 to 2049 and two simulations forced by estimated changes in solar radiation from 1700 to 1992. In the first global warming simulation, the forcing is by greenhouse gas only, while in the remaining two simulations the direct influence of sulfate aerosols is also included. From the climate change signals of the greenhouse gas only and the average of the two greenhouse gas-plus-aerosol simulations, two optimized fingerprint patterns are derived by weighting the model-predicted climate change patterns towards low-noise directions. The optimized fingerprint patterns are then applied as a filter to the observed near-surface temperature trend patterns, yielding several detection variables. The space-time structure of natural climate variability needed to determine the optimal fingerprint pattern and the resultant signal-to-noise ratio of the detection variable is estimated from several multi-century control simulations with different CGCMs and from instrumental data over the last 136 y. Applying the combined greenhouse gas-plus-aerosol fingerprint in the same way as the greenhouse gas only fingerprint in a previous work, the recent 30-y trends (1966–1995) of annual mean near surface temperature are again found to represent a significant climate change at the 97.5% confidence level. However, using both the greenhouse gas and the combined forcing fingerprints in a two-pattern analysis, a substantially better agreement between observations and the climate model prediction is found for the combined forcing simulation. Anticipating that the influence of the aerosol forcing is strongest for longer term temperature trends in summer, application of the detection and attribution test to the latest observed 50-y trend pattern of summer temperature yielded statistical consistency with the greenhouse gas-plus-aerosol simulation with respect to both the pattern and amplitude of the signal. In contrast, the observations are inconsistent with the greenhouse-gas only climate change signal at a 95% confidence level for all estimates of climate variability. The observed trend 1943–1992 is furthermore inconsistent with a hypothesized solar radiation change alone at an estimated 90% confidence level. Thus, in contrast to the single pattern analysis, the two pattern analysis is able to discriminate between different forcing hypotheses in the observed climate change signal. The results are subject to uncertainties associated with the forcing history, which is poorly known for the solar and aerosol forcing, the possible omission of other important forcings, and inevitable model errors in the computation of the response to the forcing. Further uncertainties in the estimated significance levels arise from the use of model internal variability simulations and relatively short instrumental observations (after subtraction of an estimated greenhouse gas signal) to estimate the natural climate variability. The resulting confidence limits accordingly vary for different estimates using different variability data. Despite these uncertainties, however, we consider our results sufficiently robust to have some confidence in our finding that the observed climate change is consistent with a combined greenhouse gas and aerosol forcing, but inconsistent with greenhouse gas or solar forcing alone. Received: 28 April 1996 / Accepted: 27 January 1997     

An efficient climate forecasting method using an intermediate complexity Earth System Model and the ensemble Kalman filter

We present the implementation and results of a model tuning and ensemble forecasting experiment using an ensemble Kalman filter for the simultaneous estimation of 12 parameters in a low resolution coupled atmosphere-ocean Earth System Model by tuning it to realistic data sets consisting of Levitus ocean temperature/salinity climatology, and NCEP/NCAR atmospheric temperature/humidity reanalysis data. The resulting ensemble of tuned model states is validated by comparing various diagnostics, such as mass and heat transports, to observational estimates and other model results. We show that this ensemble has a very reasonable climatology, with the 3-D ocean in particular having comparable realism to much more expensive coupled numerical models, at least in respect of these averaged indicators. A simple global warming experiment is performed to investigate the response and predictability of the climate to a change in radiative forcing, due to 100 years of 1% per annum atmospheric CO₂ increase. The equilibrium surface air temperature rise for this CO₂ increase is 4.2±0.1°C, which is approached on a time scale of 1,000 years. The simple atmosphere in this version of the model is missing several factors which, if included, would substantially increase the uncertainty of this estimate. However, even within this ensemble, there is substantial regional variability due to the possibility of collapse of the North Atlantic thermohaline circulation (THC), which switches off in more than one third of the ensemble members. For these cases, the regional temperature is not only 3–5°C colder than in the warmed worlds where the THC remains switched on, but is also 1–2°C colder than the current climate. Our results, which illustrate how objective probabilistic projections of future climate change can be efficiently generated, indicate a substantial uncertainty in the long-term future of the THC, and therefore the regional climate of western Europe. However, this uncertainty is only apparent in long-term integrations, with the initial transient response being similar across the entire ensemble. Application of this ensemble Kalman filtering technique to more complete climate models would improve the objectivity of probabilistic forecasts and hence should lead to significantly increased understanding of the uncertainty of our future climate.     

A Climate Change Scenario for the Tropics

This paper describes the construction of a climate change scenario for a region representing the extended Tropics – 30° N to 30° S – using a methodology that combines results from a simple climate model and a Global Climate Model (GCM) transient climate change experiment. The estimated date by which this climate change scenario might be realized ranges from as early as the end of the 2030s to as late as well into the 22nd century. The central estimate is for this scenario to describe the climate of the 2060s, which would represent a global warming rate of about 0.2 °C per decade, with associated atmospheric CO₂ concentrations estimated to be about 560 ppmv, 55% higher than 1990 levels. The role of anthropogenic aerosols in offsetting part of this future global warming and altering the regional character of the changes has not been considered. The paper presents changes in mean temperature; mean rainfall; rainfall seasonality, variability, frequency, and intensity and soil moisture. These patterns of change derive from only one GCM climate change experiment; different experiments would yield different patterns for the same global warming. There is also some discussion about possible changes in tropical cyclone (TC) activity, although since TCs remain poorly modelled in GCMs, the full range of possibilities (from reduced activity, through no change, to increased activity) should be considered in any impact assessment.     

Temperature above the surface layer

Three published data sets of upper-air global temperatures, two from radiosondes and one from satellites, are examined and compared for the lower stratosphere and troposphere.The global lower stratosphere exhibits a downward trend for the past 16+ years of -0.53 °C (-0.33 °C per decade). Since the 1960's (using radiosondes before 1979 which are subject to known and unknown inhomogeneities) it is likely that there has been a downward trend of about the same magnitude. Significant issues of the stratospheric radiosonde data are: (1) that the long-term time series is biased toward spurious cooling; and (2) the earliest years of Angell display unrealistic variability. Inhomogeneities in satellite data due to orbit drifting and instrument calibration are examined.The tropospheric temperature has shown a downward trend of -0.11 °C since 1979 (-0.07 °C per decade). Beginning in earlier years, (relying only on radiosonde data before 1979) the estimated warming trend since the late 1950's is +0.07 to +0.11 °C per decade.Tropospheric and surface temperature anomalies are compared. There is concern that the disproportionate representation of extratropical continents, with their high temperature variance, may bias any long term global surface trend toward a maximum-possible value than would be calculated had all regions (including those with much lower responsiveness) been monitored.     

CO2 climate sensitivity and snow-sea-ice albedo parameterization in an atmospheric GCM coupled to a mixed-layer ocean model

The snow-sea-ice albedo parameterization in an atmospheric general circulation model (GCM), coupled to a simple mixed-layer ocean and run with an annual cycle of solar forcing, is altered from a version of the same model described by Washington and Meehl (1984). The model with the revised formulation is run to equilibrium for 1 × CO₂ and 2 × CO₂ experiments. The 1 ×CO₂ (control) simulation produces a global mean climate about 1° warmer than the original version, and sea-ice extent is reduced. The model with the altered parameterization displays heightened sensitivity in the global means, but the geographical patterns of climate change due to increased carbon dioxide (CO₂) are qualitatively similar. The magnitude of the climate change is affected, not only in areas directly influenced by snow and ice changes but also in other regions of the globe, including the tropics where sea-surface temperature, evaporation, and precipitation over the oceans are greater. With the less-sensitive formulation, the global mean surface air temperature increase is 3.5 °C, and the increase of global mean precipitation is 7.12%. The revised formulation produces a globally averaged surface air temperature increase of 4.04 °C and a precipitation increase of 7.25%, as well as greater warming of the upper tropical troposphere. Sensitivity of surface hydrology is qualitatively similar between the two cases with the larger-magnitude changes in the revised snow and ice-albedo scheme experiment. Variability of surface air temperature in the model is comparable to observations in most areas except at high latitudes during winter. In those regions, temporal variation of the sea-ice margin and fluctuations of snow cover dependent on the snow-ice-albedo formulation contribute to larger-than-observed temperature variability. This study highlights an uncertainty associated with results from current climate GCMs that use highly parameterized snow-sea-ice albedo schemes with simple mixed-layer ocean models.The National Center for Atmospheric Research is sponsored by the National Science Foundation.     

Representing the atmospheric boundary layer in climate models of intermediate complexity

In this study the role of atmospheric boundary layer schemes in climate models is investigated. Including a boundary layer scheme in an Earth system model of intermediate complexity (EMIC) produces only minor differences in the estimated global distribution of sensible and latent heat fluxes over land (upto about 15% of the net radiation at the surface). However, neglecting of boundary layer processes, such as the development of a well-mixed layer over land or the impact of stability on the exchange coefficient in the surface layer, leads to erroneous surface temperatures, especially in convective conditions with low wind speeds. As these conditions occur frequently, introducing a boundary layer scheme in an EMIC gives reductions in June-July-August averaged surface temperature of 1–2 °C in wet areas, to 5–7 °C in desert areas. Even a relatively simple boundary layer scheme provides reasonable estimates of the surface fluxes and surface temperatures. Detailed schemes that solve explicitly the turbulent fluxes within the boundary layer are only required when vertical profiles of potential temperature are needed.     

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 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.     

Corrections of Systematic Errors and Data Homogenisation in the Daily Temperature Padova Series (1725–1998)

The correction, validation and homogenisation of the long temperature series of Padova (1725–1998) are discussed. After a careful historical investigation made in order to discover the metadata concerning the series, i.e., instrument features, calibrating methodologies, operational procedures (i.e., exposure, timing and number of daily observations), instrument maintenance, relocations and instrument replacements, the series has been corrected for all the systematic errors derived from any change in the instruments or the operative methodology. Above all, correction focused on instrumental drift, scale expansion, building influence, relationship between indoor-outdoor measurements, minima and maxima evaluation from observations performed at different times, homogenisation for difference of level and change of site. Statistical tests applied to the data and the comparison with other known series has clarified some uncertainties about exposure and operational procedures that the historical analysis of metadata was unable to solve. Moreover, gaps have been filled after the comparison of the series with others of neighbouring sites. The critical work of debugging, correcting, validating and homogenising the series is essential for a correct interpretation of data, as in some cases the errors that have been corrected have been found to be greater than the climate signal. Especially in the early period, the algebraic sum of the corrections of the mean daily temperature exceeds 8 ° C, where monthly corrections can reach 6 ° C. After correction, validation and homogenisation, the linear trend of the Padova series is positive, +0.31 ° C/100 yr over the period1774–today. Looking at post-industrial warming, the temperature rise is+0.44 ° C in the last 130 years, which means +0.34 ° C/100 yr, not farfrom the above bulk average.     

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.     

Prediction of Changes in Soil Moisture Associated with Climatic Changes and Their Implications for Vegetation Changes: Waves Model Simulation on Taihang Mountain, China

The WAVES model was used to simulate the effect of global warming on soil moisture on the semi-arid Taihang Mountain in China. Parameters of the WAVES model were first adjusted according to soil moisture data from a field global warming experiment. Then, the reliability of WAVES in predicting soil moisture changes induced by climatic change was confirmed by comparing the simulated and observed soil moisture values under different climatic conditions and plant growth rates of another field treatment. Next, 10 climate change scenarios incorporating increases in temperature and changes in precipitation were designed. When a simulation was conducted using the leaf area index (LAI) growth pattern from a field experiment under the present climatic conditions, the results suggested that the combination of temperature increase and precipitation decrease would greatly decrease soil water content throughout the entire simulation period. On the other hand, only when precipitation increased by 20% and temperatureincreased by 2 °C, the effect of precipitation increase on soil moisture was obviously positive. Although soil moisture conditions in the T2P1 (temperature increase by 2 °C and precipitation increase by 10%) and T4P2 (temperature increase by 4 °C and precipitation increase by 20%) scenarios were slightly better during the rainy season and notmuch changed before the rainy season, the positive effect of 10%precipitation increase on soil moisture was totally offset by moisture decrease caused bya 4 °C temperature increase in the T4P1 scenario. At the same time, the trends of soil-moisture change were highly coincident with predicted changes in productivity. Finally, the predicted LAI values from other studies were combined with the climatic change scenarios and used in the simulation. The results showed that changes in LAI alleviated, at least to some extent, the effects of temperature and precipitation changes on soil moisture.