Slowing the increase of atmospheric carbon dioxide: A biological approach

Planting trees to act as carbon sinks has been suggested as a way to slow the increase of atmospheric CO₂. Forestry growth and yield models were used to estimate that it would take 192 million hectares of Douglas-fir (Pseudotsuga menziesii) or 250 million hectares of Loblolly pine (Pinus taeda) to capture and store the United States' anthropogenic carbon emissions for an assumed period of 50 yr, at current emission rates. Although maximum growth rates are similar for both species, Douglas-fir requires less area because of its greater ability to store carbon, and its ability to maintain a high growth rate for a longer period of time. The usefulness of a particular species also depends in part on the length of the planning horizon and the forestry project. For periods of 50 or more years, it is important to consider a species' cumulative carbon storage potential rather than its potential maximum growth rate at some point during its life cycle. Forestation (reforestation and afforestation) appears to be feasible as a possible component of a comprehensive strategy for managing the CO₂ problem, but it must be practiced globally to be effective.The research described in this article has been funded by the U.S. Environmental Protection Agency. This document has been prepared at the EPA Environmental Research Laboratory. in Corvallis, Oregon, through contract number 68-C8-0006 to NSI Technology Inc. It has been subjected to the Agency's peer and administrative review and approved for publication. Mention of trade names or commercial products does not constitute endorsement of recommendation for use.     

Economic drivers of migration and climate change in LDCs

Environmental migration is a topic which has given rise to widespread debate and gloomy predictions about the state of the world in 2050, but where rigorous research and empirical evidence are unfortunately in short supply. In this paper, we review the existing research on and empirical evidence of how climate change and climate variability in Less Developed Countries (LDCs) affects two main drivers of migration identified by migration models in the economic literature, namely income level differentials between origin and destination areas and income variability in origin areas, and how they in turn affect migration. We find that there are serious gaps in both the economic and the environmental literature that render it impossible to make sound and robust predictions of how climate change and increased climate variability will affect the economic migration drivers, and of how these in turn may change existing migration patterns. There are some empirical indications that income differentials may increase due to lower income levels in the origin areas of LDCs, but virtually no evidence exists of the effects of climate change or increased climate variability on income variability. Furthermore, although a negative relationship between migration and rainfall has been established by many researchers, there is only very limited evidence as to what drives it. A clearer picture of the driving force behind the link between rainfall and migration would greatly benefit policymaking in this area.     

Effects of Climate Change on Rice Production in the Tropical Humid Climate of Kerala, India

The CERES-Rice v3. crop simulation model, calibrated and validated for its suitability to simulate rice production in the tropical humid climate Kerala State of India, is used for analysing the effect of climate change on rice productivity in the state. The plausible climate change scenario for the Indian subcontinent as expected by the middle of the next century, taking into account the projected emissions of greenhouse gases and sulphate aerosols, in a coupled atmosphere-ocean model experiment performed at Deutsches Klimarechenzentrum, Germany, is adopted for the study. The adopted scenario represented an increase in monsoon seasonal mean surface temperature of the order of about 1.5°C, and an increase in rainfall of the order of 2 mm per day, over the state of Kerala in the decade 2040–2049 with respect to the 1980s. The IPCC Business-as-usual scenario projection of plant usable concentration of CO₂ about 460 PPM by the middle of the next century are also used in the crop model simulation. On an average over the state with the climate change scenario studied, the rice maturity period is projected to shorten by 8% and yield increase by 12%. When temperature elevations only are taken into consideration, the crop simulations show a decrease of 8% in crop maturity period and 6% in yield. This shows that the increase in yield due to fertilisation effect of elevated CO₂ and increased rainfall over the state as projected in the climate change scenario nearly makes up for the negative impact on rice yield due to temperature rise. The sensitivity experiments of the rice model to CO₂ concentration changes indicated that over the state, an increase in CO₂ concentration leads to yield increase due to its fertilisation effect and also enhance the water use efficiency of the paddy. The temperature sensitivity experiments have shown that for a positive change in temperature up to 5°C, there is a continuous decline in the yield. For every one degree increment the decline in yield is about 6%. Also, in another experiment it is observed that the physiological effect of ambient CO₂ at 425 ppm concentration compensated for the yield losses due to increase in temperature up to 2°C. Rainfall sensitivity experiments have shown that increase in rice yield due to increase in rainfall above the observed values is near exponential. But decrease in rainfall results in yield loss at a constant rate of about 8% per 2 mm/day, up to about 16 mm/day.     

Anomalies in temperature and rainfall during warm Arctic seasons as a guide to the formulation of climate scenarios

Recently much concern has been expressed regarding the impact of an increased atmospheric CO₂ concentration on climate. Unfortunately, present understanding and models of the climate system are not good enough for reliable prediction of such impacts. This paper presents an analysis of recent climate data in order to illustrate the nature of regional temperature and rainfall changes in different seasons and to provide some guidance with regard to points which might be borne in mind when scenarios of future climate (especially those taking into account human impacts) are being formulated.Since it is believed that an increased atmospheric CO₂ concentration will cause a warming and models and data suggest that the Arctic is more sensitive to climatic change than other latitudes, anomalies associated with warm Arctic seasons have been studied.The regional temperature, precipitation and pressure anomalies in the northern hemisphere for the 10 warmest Arctic winters and 10 warmest Arctic summers during the last 70 years have been investigated. Even when the Arctic area is warm, there are circulation changes such that large coherent anomalies occur elsewhere, with some regions warming and some cooling. The 10 warmest Arctic winters were characterised by larger amplitude anomalies, in the Arctic and elsewhere, than the 10 warmest summers, illustrating the difference in response between seasons. The precipitation differences for the 10 warmest Arctic winters and summers show for North America large coherent areas of increase or decrease, which again differ according to season. However, in winter the differences are not statistically significant, while the differences in two areas are significant in summer.     

Modelling the sensitivity of wheat growth and water balance to climate change in Southeast Australia

A simulation study was carried out to assess the potential sensitivity of wheat growth and water balance components to likely climate change scenarios at Wagga Wagga, NSW, Australia. Specific processes considered include crop development, growth rate, grain yield, water use efficiency, evapotranspiration, runoff and deep drainage. Individual impacts of changes in temperature, rainfall and CO₂ concentration ([CO₂]) and the combined impacts of these three variables were analysed for 2050 ([CO₂] = 570 ppm, T +2.3°C, P ?7%) and 2070 ([CO₂] = 720 ppm, T +3.8°C, P ?10%) conditions. Two different rainfall change scenarios (changes in rainfall intensity or rainfall frequency) were used to modify historical rainfall data. The Agricultural Production Systems Simulator (APSIM) was used to simulate the growth and water balance processes for a 117 year period of baseline, 2050 and 2070 climatic conditions. The results showed that wheat yield reduction caused by 1°C increase in temperature and 10% decrease in rainfall could be compensated by a 266 ppm increase in [CO₂] assuming no interactions between the individual effects. Temperature increase had little impact on long-term average water balance, while [CO₂] increase reduced evapotranspiration and increased deep drainage. Length of the growing season of wheat decreased 22 days in 2050 and 35 days in 2070 conditions as a consequence of 2.3°C and 3.8°C increase in temperature respectively. Yield in 2050 was approximately 1% higher than the simulated baseline yield of 4,462 kg ha^???1, but it was 6% lower in 2070. An early maturing cultivar (Hartog) was more sensitive in terms of yield response to temperature increase, while a mid-maturing cultivar (Janz) was more sensitive to rainfall reduction. Janz could benefit more from increase in CO₂ concentration. Rainfall reduction across all rainfall events would have a greater negative impact on wheat yield and WUE than if only smaller rainfall events reduced in magnitude, even given the same total decrease in annual rainfall. The greater the reduction in rainfall, the larger was the difference. The increase in temperature increased the difference of impact between the two rainfall change scenarios while increase in [CO₂] reduced the difference.     

A carbon budget for Brazil: Influence of future land-use change

Because of its large area of high C density forests and high deforestation rate, Brazil may play an important role in the global C cycle. The study reported here developed an estimate of Brazil's biotic CO₂-C budget for the period 1990–2010. The analysis used a spreadsheet C accounting model based on three major components: a conceptual model of ecosystem C cycling, a recently completed vegetation classification developed from remote-sensing data, and published estimates of C density for each of the vegetation classes. The dynamics of the model came from estimates of disturbance to ecosystems that release C and estimates of recovery from past disturbance that store C. The model was projected into the future with three alternative estimates of the rate of future land use change. Under all three deforestation scenarios Brazil was a C source in the range of about 3–5 × 10⁹ MgC over the 20-yr study period.The research described in this article has been funded wholly by the U.S. Environmental Protection Agency. This document has been prepared at the EPA National Health and Environmental Effects Research Laboratory in Corvallis, OR, U.S.A., through contract number 68-C8-0006 to ManTech Environmental Research Services, Corp. It has been subjected to the Agency's peer and administrative review and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.     

Assessing the impacts of climate change on rice yields in the main rice areas of China

This paper assesses the impact of climate change on irrigated rice yield using B2 climate change scenario from the Regional Climate Model (RCM) and CERES-rice model during 2071--2090. Eight typical rice stations ranging in latitude, longitude, and elevation that are located in the main rice ecological zones of China are selected for impact assessment. First, Crop Estimation through Resource and Environment Synthesis (CERES)-rice model is validated using farm experiment data in selected stations. The simulated results represent satisfactorily the trend of flowering duration and yields. The deviation of simulation within ± 10% of observed flowering duration and ± 15% of observed yield. Second, the errors of the outputs of RCM due to the difference of topography between station point and grid point is corrected. The corrected output of the RCM used for simulating rice flowering duration and yield is more reliable than the not corrected. Without CO₂ direct effect on crop, the results from the assessment explore that B2 climate change scenario would have a negative impact on rice yield at most rice stations and have little impacts at Fuzhou and Kunming. To find the change of inter-annual rice yield, a preliminary assessment is made based on comparative cumulative probability at low and high yield and the coefficient variable of yield between the B2 scenario and baseline. Without the CO₂ direct effect on rice yield, the result indicates that frequency for low yield would increase and it reverses for high yield, and the variance for rice yield would increase. It is concluded that high frequency at low yield and high variances of rice yield could pose a threat to rice yield at most selected stations in the main rice areas of China. With the CO₂ direct effect on rice yield, rice yield increase in all selected stations.     

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.     

Sub-Saharan Northern African climate at the end of the twenty-first century: forcing factors and climate change processes

A regional climate model, the Weather Research and Forecasting (WRF) Model, is forced with increased atmospheric CO₂ and anomalous SSTs and lateral boundary conditions derived from nine coupled atmosphere–ocean general circulation models to produce an ensemble set of nine future climate simulations for northern Africa at the end of the twenty-first century. A well validated control simulation, agreement among ensemble members, and a physical understanding of the future climate change enhance confidence in the predictions. The regional model ensembles produce consistent precipitation projections over much of northern tropical Africa. A moisture budget analysis is used to identify the circulation changes that support future precipitation anomalies. The projected midsummer drought over the Guinean Coast region is related partly to weakened monsoon flow. Since the rainfall maximum demonstrates a southward bias in the control simulation in July–August, this may be indicative of future summer drying over the Sahel. Wetter conditions in late summer over the Sahel are associated with enhanced moisture transport by the West African westerly jet, a strengthening of the jet itself, and moisture transport from the Mediterranean. Severe drought in East Africa during August and September is accompanied by a weakened Indian monsoon and Somali jet. Simulations with projected and idealized SST forcing suggest that overall SST warming in part supports this regional model ensemble agreement, although changes in SST gradients are important over West Africa in spring and fall. Simulations which isolate the role of individual climate forcings suggest that the spatial distribution of the rainfall predictions is controlled by the anomalous SST and lateral boundary conditions, while CO₂ forcing within the regional model domain plays an important secondary role and generally produces wetter conditions.     

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.     

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.     

The role of closed ecological systems in carbon cycle modelling

Acquiring a mechanistic understanding of the role of biotic feedbacks for the links between atmospheric CO₂ concentrations and temperature is essential for trustworthy climate predictions. Currently, computer-based simulations are the only available tool to estimate the global impact of biotic feedbacks on future atmospheric CO₂ and temperatures. Here we propose an alternative and complementary approach by using materially closed, energetically open analogue/physical models of the carbon cycle. We argue that there is unexplored potential in using a materially closed approach to improve our understanding of the magnitude and direction of many biotic carbon feedbacks and that recent technological advances make this feasible. We also suggest how such systems could be designed and discuss the advantages and limitations of establishing physical models of the global carbon cycle.     

Probabilistic simulations of the impact of increasing leaf-level atmospheric carbon dioxide on the global land surface

Using a climate model with a sophisticated land surface scheme, simulations were conducted to explore the impact of increases in leaf-level carbon dioxide (CO₂) on evaporation, temperature and other land surface quantities. Fifty-one realizations were run, for each of four Januarys and four Julys for CO₂ concentrations at leaf-level of 280, 375, 500, 650, 840 and 1,000 ppmv. Atmospheric CO₂ concentration was held constant at 375 ppmv in all experiments. Statistically significant decreases in evaporation and increases in temperature occur in specific regions as leaf-level CO₂ is increased from 280 to 375 ppmv. These same areas expand geographically, and the magnitude of the changes increase as leaf-level CO₂ is increased further suggesting that changes are caused by the increase in leaf-level CO₂ and are not internal model variability. As leaf-level CO₂ is increased further, larger areas of the continental surface are affected by increasing amounts and a statistically significant change in precipitation is seen. The increase in leaf-level CO₂ from 280 ppmv to 375 ppmv causes statistically significant changes in the evaporation over 12% of continental surfaces in July. This increases to 25% at 500 ppmv, 35% at 650 ppmv, 41% at 840 ppmv and 47% at 1,000 ppmv. This affects temperature and rainfall by similar amounts, generally in coincident regions. An analysis of these results over key regions shows that the probability density functions of the latent heat flux and temperature are affected non-uniformly. There is a shift in the latent heat flux probability density function to lower values, mainly through the reduction in the upper tail of the distribution. The temperature probability density function shifts to higher values, mainly through an increase in the upper tail of the distribution indicating that the impact is focussed on extremes. Given that there are a suite of well evaluated land surface models that include the biogeochemical effects of increasing CO₂ we suggest that the inclusion of such a model should be a recommended component of climate models used in future assessment reports by the Intergovernmental Panel on Climate Change.     

Spatial impact of projected changes in rainfall and temperature on wheat yields in Australia

Climate projections over the next two to four decades indicate that most of Australia’s wheat-belt is likely to become warmer and drier. Here we used a shire scale, dynamic stress-index model that accounts for the impacts of rainfall and temperature on wheat yield, and a range of climate change projections from global circulation models to spatially estimate yield changes assuming no adaptation and no CO₂ fertilisation effects. We modelled five scenarios, a baseline climate (climatology, 1901–2007), and two emission scenarios (“low” and “high” CO₂) for two time horizons, namely 2020 and 2050. The potential benefits from CO₂ fertilisation were analysed separately using a point level functional simulation model. Irrespective of the emissions scenario, the 2020 projection showed negligible changes in the modelled yield relative to baseline climate, both using the shire or functional point scale models. For the 2050-high emissions scenario, changes in modelled yield relative to the baseline ranged from ?5 % to +6 % across most of Western Australia, parts of Victoria and southern New South Wales, and from ?5 to ?30 % in northern NSW, Queensland and the drier environments of Victoria, South Australia and in-land Western Australia. Taking into account CO₂ fertilisation effects across a North–south transect through eastern Australia cancelled most of the yield reductions associated with increased temperatures and reduced rainfall by 2020, and attenuated the expected yield reductions by 2050.     

Using El Niño/Southern Oscillation Climate Data to Predict Rice Production in Indonesia

Despite the strong signal of El Niño/Southern Oscillation (ENSO) events on climate in the Indo-Pacific region, models linking ENSO-based climate variability to seasonal rice production and food security in the region have not been well developed or widely used in a policy context. This study successfully measures the connections among sea surface temperature anomalies (SSTAs), rainfall, and rice production in Indonesia during the past three decades. Regression results show particularly strong connections on Java, where 55% of the country's rice is grown. Two-thirds of the interannual variance in rice plantings and 40% of the interannual variance in rice production during the main (wet) season on Java are explained by year-to-year fluctuations in SSTAs measured 4 and 8 months in advance, respectively. These effects are cumulative; during strong El Niño years, production shortfalls in the wet season are not made up later in the crop year. The analysis demonstrates that quantitative predictions of ENSO's effects on rice harvests can provide an additional tool for managing food security in one of the world's most populous and important rice-producing countries.     

Has climate change opened new opportunities for wheat cropping in Argentina?

As a result of climate change, and in particular rainfall changes, agricultural production is likely to change across the globe. Until now most research has focused on areas which will become unsustainable for agricultural production. However, there are also regions where climate change might actually improve conditions for growth. In the western Pampas region of Argentina, average annual rainfall has increased by 100–200 mm over the last 70 years, mainly during summer. Wheat is grown during winter, primarily on stored soil water and the main factor limiting plant production in this area is rainfall. Using the well tested simulation model APSIM-NWheat, we studied whether recent climate change has potentially opened new opportunities for wheat cropping in Argentina. Simulation results indicated that the additional rainfall in the Pampas of Argentina has increased the achievable yield (defined as the yield limited by solar radiation, temperature, water and nitrogen supply) of wheat in the currently cropped region, but less than expected based on the large amount of additional rainfall. The higher achievable yield from additional rainfall could potentially allow an expansion of profitable wheat cropping into currently non-cropped areas, where the achievable wheat yield increased in average from 1 t/ha to currently 2 t/ha. However, the poor water-holding capacity of the sandy soils which dominate the region outside the current cropping area limits the systems ability to use most of the increased summer rainfall. Nevertheless, the current higher achievable yield indicates a suitability of the region for cropping, which will slightly decline or remain unchanged depending on summer rainfall storage, with current and future climate change, including projected changes in rainfall, temperature and atmospheric CO₂ concentration. Factors other than just the achievable yield will eventually influence any future development of this region for cropping, including the high sensitivity of the sandy soils to erosion and nutrient leaching, current relatively high land prices, restrictions on clearing for cropping, the distance to the nearest port and current unsuitable cultivars withstanding the high frost risk.     

The usability of climate information in sub-national planning in India,Kenya and Uganda: the role of social learning and intermediary organisations

Correctly estimating the effect of elevated CO₂ (eCO₂) on biomass production is paramount for accurately projecting agricultural productivity, global carbon balances and climate changes. Plant physiology suggests that eCO₂ should result in a strongly positive CO₂ fertilisation effect (CFE) via positive effects on photosynthesis and water use efficiency. However, the CFE in CO₂ experiments is often constrained because of other factors of which rainfall pattern is particularly important. Here, we apply a generally applicable, empirically derived relationship between the CFE and an index of seasonal rainfall balance (SRB), to identify how historical and projected future rainfall patterns modify the CFE using 25 native grassland sites in south-eastern (SE) Australia as a test case. We found that historical and projected rainfall produced SRBs that varied widely from year-to-year resulting in a CFE that was only positive in about 40% of years, with no or even negative biomass responses in the remainder of years; a finding that is in marked contrast to other studies that have not taken account of relationships between rainfall seasonality and plant responses to CO₂. The dependence of the CFE on SRB also means that using the CFE from a specific eCO₂ experiment can be misleading as the result will be heavily influenced by the SRB during the period of experimentation but this problem can be avoided by using a robust general relationship of the kind used in this study. Generalisations of grassland biomass responses to the rising CO₂ concentration are contextual in terms of the variability in precipitation seasonality; as such, this provides a new lens by which to view aboveground responses to the rising CO₂ concentration and fosters a novel approach for cross-site comparisons among experiments.     

Improvement of model forecast on the Asian summer rainfall anomaly with the application of a spatial filtering scheme

Summary A revised 25-point Shuman-Shapiro Spatial Filter (RSSSF) has been applied to six atmospheric circulation models and multi-model ensemble (MME) predictions, and its effect on the improvement of model forecast skill scores of the Asian summer precipitation anomaly is discussed in this paper. On the basis of 21-yr model ensemble predictions, the RSSSF can remove the unpredictable ‘noise’ with respect to the 2-grid wavelength in the model precipitation anomaly fields and maintain the large-scale counterpart, which is related to the response of the model to large-scale boundary forcing. Therefore, this could possibly enhance the forecast skill of the Asian summer rainfall anomaly in the models and the MME. The potential improvement of model forecasting skill is found in the Asian summer monsoon region, where the anomaly correlation coefficient (ACC) has been improved by 7–40%, corresponding to the decreased root mean square error (RMSE) in the model and the MME precipitation anomaly forecasts.     

Great Lakes Hydrology Under Transposed Climates

Historical climates, based on 43 years of daily data from areas south and southwest of the Great Lakes, were used to examine the hydrological response of the Great Lakes to warmer climates. The Great Lakes Environmental Research Laboratory used their conceptual models for simulating moisture storages in, and runoff from, the 121 watersheds draining into the Great Lakes, over-lake precipitation into each lake, and the heat storages in, and evaporation from, each lake. This transposition of actual climates incorporates natural changes in variability and timing within the existing climate; this is not true for General Circulation Model-generated corrections applied to existing historical data in many other impact studies. The transposed climates lead to higher and more variable over-land evapotranspiration and lower soil moisture and runoff with earlier runoff peaks since the snow pack is reduced up to 100%. Water temperatures increase and peak earlier. Heat resident in the deep lakes increases throughout the year. Buoyancy-driven water column turnover frequency drops and lake evaporation increases and spreads more throughout the annual cycle. The response of runoff to temperature and precipitation changes is coherent among the lakes and varies quasi-linearly over a wide range of temperature changes, some well beyond the range of current GCM predictions for doubled CO₂ conditions.     

Global climatic change,hurricanes, and a tropical forest

Most, if not all forests in the Caribbean are subject to occasional disturbances from hurricanes. If current general circulation model (GCM) predictions are correct, with doubled atmospheric CO₂ (2 × CO₂), the tropical Atlantic will be between 1 °C and 4 °C warmer than it is today. With such a warming, more than twice as many hurricanes per year could be expected in the Caribbean. Furthermore, Emanuael (1987) indicates that in a warmed world the destructive potential of Atlantic hurricanes could be increased by 40% to 60%. While speculative, these increases would dramatically change the disturbance regimes affecting tropical forests in the region and might alter forest structure and composition. Global warming impacts through increased hurricane damage on Caribbean forests are presented.An individual tree, gap dynamics forest ecosystem model was used to simulate the range of possible hurricane disturbance regimes which could affect the Luquillo Experimental Forest in Puerto Rico. Model storm frequency ranged from no storms at all up to one storm per year; model storm intensity varied from no damage up to 100% mortality of trees. The model does not consider the effects of changing temperature and rainfall patterns on the forest. Simulation results indicate that with the different hurricane regimes a range of forest types are possible, ranging from mature forest with large trees, to an area in which forest trees are never allowed to reach maturity.