Climate-related uncertainties in projections of the twenty-first century terrestrial carbon budget: off-line model experiments using IPCC greenhouse-gas scenarios and AOGCM climate projections

A terrestrial ecosystem model (Sim-CYCLE) was driven by multiple climate projections to investigate uncertainties in predicting the interactions between global environmental change and the terrestrial carbon cycle. Sim-CYCLE has a spatial resolution of 0.5°, and mechanistically evaluates photosynthetic and respiratory CO₂ exchange. Six scenarios for atmospheric-CO₂ concentrations in the twenty-first century, proposed by the Intergovernmental Panel on Climate Change, were considered. For each scenario, climate projections by a coupled atmosphere–ocean general circulation model (AOGCM) were used to assess the uncertainty due to socio-economic predictions. Under a single CO₂ scenario, climate projections with seven AOGCMs were used to investigate the uncertainty stemming from uncertainty in the climate simulations. Increases in global photosynthesis and carbon storage differed considerably among scenarios, ranging from 23 to 37% and from 24 to 81 Pg C, respectively. Among the AOGCM projections, increases ranged from 26 to 33% and from 48 to 289 Pg C, respectively. There were regional heterogeneities in both climatic change and carbon budget response, and different carbon-cycle components often responded differently to a given environmental change. Photosynthetic CO₂ fixation was more sensitive to atmospheric CO₂, whereas soil carbon storage was more sensitive to temperature. Consequently, uncertainties in the CO₂ scenarios and climatic projections may create additional uncertainties in projecting atmospheric-CO₂ concentrations and climates through the interactive feedbacks between the atmosphere and the terrestrial ecosystem.     

Identifying uncertainties in Arctic climate change projections

Wide ranging climate changes are expected in the Arctic by the end of the 21st century, but projections of the size of these changes vary widely across current global climate models. This variation represents a large source of uncertainty in our understanding of the evolution of Arctic climate. Here we systematically quantify and assess the model uncertainty in Arctic climate changes in two CO₂ doubling experiments: a multimodel ensemble (CMIP3) and an ensemble constructed using a single model (HadCM3) with multiple parameter perturbations (THC-QUMP). These two ensembles allow us to assess the contribution that both structural and parameter variations across models make to the total uncertainty and to begin to attribute sources of uncertainty in projected changes. We find that parameter uncertainty is an major source of uncertainty in certain aspects of Arctic climate. But also that uncertainties in the mean climate state in the 20th century, most notably in the northward Atlantic ocean heat transport and Arctic sea ice volume, are a significant source of uncertainty for projections of future Arctic change. We suggest that better observational constraints on these quantities will lead to significant improvements in the precision of projections of future Arctic climate change.     

A comparison of PMIP2 model simulations and the MARGO proxy reconstruction for tropical sea surface temperatures at last glacial maximum

Results from multiple model simulations are used to understand the tropical sea surface temperature (SST) response to the reduced greenhouse gas concentrations and large continental ice sheets of the last glacial maximum (LGM). We present LGM simulations from the Paleoclimate Modelling Intercomparison Project, Phase 2 (PMIP2) and compare these simulations to proxy data collated and harmonized within the Multiproxy Approach for the Reconstruction of the Glacial Ocean Surface Project (MARGO). Five atmosphere–ocean coupled climate models (AOGCMs) and one coupled model of intermediate complexity have PMIP2 ocean results available for LGM. The models give a range of tropical (defined for this paper as 15°S–15°N) SST cooling of 1.0–2.4°C, comparable to the MARGO estimate of annual cooling of 1.7 ± 1°C. The models simulate greater SST cooling in the tropical Atlantic than tropical Pacific, but interbasin and intrabasin variations of cooling are much smaller than those found in the MARGO reconstruction. The simulated tropical coolings are relatively insensitive to season, a feature also present in the MARGO transferred-based estimates calculated from planktonic foraminiferal assemblages for the Indian and Pacific Oceans. These assemblages indicate seasonality in cooling in the Atlantic basin, with greater cooling in northern summer than northern winter, not captured by the model simulations. Biases in the simulations of the tropical upwelling and thermocline found in the preindustrial control simulations remain for the LGM simulations and are partly responsible for the more homogeneous spatial and temporal LGM tropical cooling simulated by the models. The PMIP2 LGM simulations give estimates for the climate sensitivity parameter of 0.67°–0.83°C per Wm⁻², which translates to equilibrium climate sensitivity for doubling of atmospheric CO₂ of 2.6–3.1°C.     

Simulation of long-term future climate changes with the green McGill paleoclimate model: the next glacial inception

The multi-component “green” McGill Paleoclimate Model (MPM), which includes interactive vegetation, is used to simulate the next glacial inception under orbital and prescribed atmospheric CO₂ forcing. This intermediate complexity model is first run for short-term periods with an increasing atmospheric CO₂ concentration; the model's response is in general agreement with the results of GCMs for CO₂ doubling. The green MPM is then used to derive projections of the climate for the next 100 kyr. Under a constant CO₂ level, the model produces three types of evolution for the ice volume: an imminent glacial inception (low CO₂ levels), a glacial inception in 50 kyr (CO₂ levels of 280 or 290 ppm), or no glacial inception during the next 100 kyr (CO₂ levels of 300 ppm and higher). This high sensitivity to the CO₂ level is due to the exceptionally weak future variations of the summer insolation at high northern latitudes. The changes in vegetation re-inforce the buildup of ice sheets after glacial inception. Finally, if an initial global warming episode of finite duration is included, after which the atmospheric CO₂ level is assumed to stabilize at 280, 290 or 300 ppm, the impact of this warming is seen only in the first 5 kyr of the run; after this time the response is insensitive to the early warming perturbation.     

Past and future polar amplification of climate change: climate model intercomparisons and ice-core constraints

Climate model simulations available from the PMIP1, PMIP2 and CMIP (IPCC-AR4) intercomparison projects for past and future climate change simulations are examined in terms of polar temperature changes in comparison to global temperature changes and with respect to pre-industrial reference simulations. For the mid-Holocene (MH, 6,000 years ago), the models are forced by changes in the Earth’s orbital parameters. The MH PMIP1 atmosphere-only simulations conducted with sea surface temperatures fixed to modern conditions show no MH consistent response for the poles, whereas the new PMIP2 coupled atmosphere–ocean climate models systematically simulate a significant MH warming both for Greenland (but smaller than ice-core based estimates) and Antarctica (consistent with the range of ice-core based range). In both PMIP1 and PMIP2, the MH annual mean changes in global temperature are negligible, consistent with the MH orbital forcing. The simulated last glacial maximum (LGM, 21,000 years ago) to pre-industrial change in global mean temperature ranges between 3 and 7°C in PMIP1 and PMIP2 model runs, similar to the range of temperature change expected from a quadrupling of atmospheric CO₂ concentrations in the CMIP simulations. Both LGM and future climate simulations are associated with a polar amplification of climate change. The range of glacial polar amplification in Greenland is strongly dependent on the ice sheet elevation changes prescribed to the climate models. All PMIP2 simulations systematically underestimate the reconstructed glacial–interglacial Greenland temperature change, while some of the simulations do capture the reconstructed glacial–interglacial Antarctic temperature change. Uncertainties in the prescribed central ice cap elevation cannot account for the temperature change underestimation by climate models. The variety of climate model sensitivities enables the exploration of the relative changes in polar temperature with respect to changes in global temperatures. Simulated changes of polar temperatures are strongly related to changes in simulated global temperatures for both future and LGM climates, confirming that ice-core-based reconstructions provide quantitative insights on global climate changes. An erratum to this article can be found at     

Probabilistic climate change projections using neural networks

Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm^–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity.     

Radiative forcing and response of a GCM to ice age boundary conditions: cloud feedback and climate sensitivity

 A general circulation model is used to examine the effects of reduced atmospheric CO₂, insolation changes and an updated reconstruction of the continental ice sheets at the Last Glacial Maximum (LGM). A set of experiments is performed to estimate the radiative forcing from each of the boundary conditions. These calculations are used to estimate a total radiative forcing for the climate of the LGM. The response of the general circulation model to the forcing from each of the changed boundary conditions is then investigated. About two-thirds of the simulated glacial cooling is due to the presence of the continental ice sheets. The effect of the cloud feedback is substantially modified where there are large changes to surface albedo. Finally, the climate sensitivity is estimated based on the global mean LGM radiative forcing and temperature response, and is compared to the climate sensitivity calculated from equilibrium experiments with atmospheric CO₂ doubled from present day concentration. The calculations here using the model and palaeodata support a climate sensitivity of about 1 Wm^-2 K^-1 which is within the conventional range. Received: 8 February 1997 / Accepted: 4 June 1997     

A probabilistic calibration of climate sensitivity and terrestrial carbon change in GENIE-1

In order to investigate Last Glacial Maximum and future climate, we “precalibrate” the intermediate complexity model GENIE-1 by applying a rejection sampling approach to deterministic emulations of the model. We develop ~1,000 parameter sets which reproduce the main features of modern climate, but not precise observations. This allows a wide range of large-scale feedback response strengths which generally encompass the range of GCM behaviour. We build a deterministic emulator of climate sensitivity and quantify the contributions of atmospheric (±0.93°C, 1σ) vegetation (±0.32°C), ocean (±0.24°C) and sea–ice (±0.14°C) parameterisations to the total uncertainty. We then perform an LGM-constrained Bayesian calibration, incorporating data-driven priors and formally accounting for structural error. We estimate climate sensitivity as likely (66% confidence) to lie in the range 2.6–4.4°C, with a peak probability at 3.6°C. We estimate LGM cooling likely to lie in the range 5.3–7.5°C, with a peak probability at 6.2°C. In addition to estimates of global temperature change, we apply our ensembles to derive LGM and 2xCO₂ probability distributions for land carbon storage, Atlantic overturning and sea–ice coverage. Notably, under 2xCO₂ we calculate a probability of 37% that equilibrium terrestrial carbon storage is reduced from modern values, so the land sink has become a net source of atmospheric CO₂.     

Uncertainty and learning: implications for the trade-off between short-lived and long-lived greenhouse gases

The economic benefits of a multi-gas approach to climate change mitigation are clear. However, there is still a debate on how to make the trade-off between different greenhouse gases (GHGs). The trade-off debate has mainly centered on the use of Global Warming Potentials (GWPs), governing the trade-off under the Kyoto Protocol, with results showing that the cost-effective valuation of short-lived GHGs, like methane (CH₄), should be lower than its current GWP value if the ultimate aim is to stabilize the anthropogenic temperature change. However, contrary to this, there have also been proposals that early mitigation mainly should be targeted on short-lived GHGs. In this paper we analyze the cost-effective trade-off between a short-lived GHG, CH₄, and a long-lived GHG, carbon dioxide (CO₂), when a temperature target is to be met, taking into consideration the current uncertainty of the climate sensitivity as well as the likelihood that this will be reduced in the future. The analysis is carried out using an integrated climate and economic model (MiMiC) and the results from this model are explored and explained using a simplified analytical economic model. The main finding is that the introduction of uncertainty and learning about the climate sensitivity increases the near-term cost-effective valuation of CH₄ relative to CO₂. The larger the uncertainty span, the higher the valuation of the short-lived gas. For an uncertainty span of ±1°C around an expected climate sensitivity of 3°C, CH₄ is cost-effectively valued 6.8 times as high as CO₂ in year 2005. This is almost twice as high as the valuation in a deterministic case, but still significantly lower than its GWP₁₀₀ value.     

The influence of continental ice,atmospheric CO2, and land albedo on the climate of the last glacial maximum

The contributions of expanded continental ice, reduced atmospheric CO₂, and changes in land albedo to the maintenance of the climate of the last glacial maximum (LGM) are examined. A series of experiments is performed using an atmosphere-mixed layer ocean model in which these changes in boundary conditions are incorporated either singly or in combination. The model used has been shown to produce a reasonably realistic simulation of the reduced temperature of the LGM (Manabe and Broccoli 1985b). By comparing the results from pairs of experiments, the effects of each of these environmental changes can be determined.Expanded continental ice and reduced atmospheric CO₂ are found to have a substantial impact on global mean temperature. The ice sheet effect is confined almost exclusively to the Northern Hemisphere, while lowered CO₂ cools both hemispheres. Changes in land albedo over ice-free areas have only a minor thermal effect on a global basis. The reduction of CO₂ content in the atmosphere is the primary contributor to the cooling of the Southern Hemisphere. The model sensitivity to both the ice sheet and CO₂ effects is characterized by a high latitude amplification and a late autumn and early winter maximum.Substantial changes in Northern Hemisphere tropospheric circulation are found in response to LGM boundary conditions during winter. An amplified flow pattern and enhanced westerlies occur in the vicinity of the North American and Eurasian ice sheets. These alterations of the tropospheric circulation are primarily the result of the ice sheet effect, with reduced CO₂ contributing only a slight amplification of the ice sheet-induced pattern.     

Future changes of the terrestrial ecosystem based on a dynamic vegetation model driven with RCP8.5 climate projections from 19 GCMs

Future changes of terrestrial ecosystems due to changes in atmospheric CO₂ concentration and climate are subject to a large degree of uncertainty, especially for vegetation in the Tropics. Here, we evaluate the natural vegetation response to projected future changes using an improved version of a dynamic vegetation model (CLM-CN-DV) driven with climate change projections from 19 global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The simulated equilibrium vegetation distribution under historical climate (1981–2000) has been compared with that under the projected future climate (2081–2100) scenario for Representative Concentration Pathway 8.5 (RCP8.5) to qualitatively assess how natural potential vegetation might change in the future. With one outlier excluded, the ensemble average of vegetation changes corresponding to climates of 18 GCMs shows a poleward shift of forests in northern Eurasia and North America, which is consistent with findings from previous studies. It also shows a general “upgrade” of vegetation type in the Tropics and most of the temperate zones, in the form of deciduous trees and shrubs taking over C3 grass in Europe and broadleaf deciduous trees taking over C4 grasses in Central Africa and the Amazon. LAI and NPP are projected to increase in the high latitudes, southeastern Asia, southeastern North America, and Central Africa. This results from CO₂ fertilization, enhanced water use efficiency, and in the extra-tropics warming. However, both LAI and NPP are projected to decrease in the Amazon due to drought. The competing impacts of climate change and CO₂ fertilization lead to large uncertainties in the projection of future vegetation changes in the Tropics.     

Uncertainty in the 2°C warming threshold related to climate sensitivity and climate feedback

Climate sensitivity is an important index that measures the relationship between the increase in greenhouse gases and the magnitude of global warming. Uncertainties in climate change projection and climate modeling are mostly related to the climate sensitivity. The climate sensitivities of coupled climate models determine the magnitudes of the projected global warming. In this paper, the authors thoroughly review the literature on climate sensitivity, and discuss issues related to climate feedback processes and the methods used in estimating the equilibrium climate sensitivity and transient climate response (TCR), including the TCR to cumulative CO₂ emissions. After presenting a summary of the sources that affect the uncertainty of climate sensitivity, the impact of climate sensitivity on climate change projection is discussed by addressing the uncertainties in 2°C warming. Challenges that call for further investigation in the research community, in particular the Chinese community, are discussed.     

Uncertainties in projected runoff over the conterminous United States

Projections of runoff from global multi-model ensembles provide a valuable basis for the estimation of future hydrological extremes. However, projections suffer from uncertainty that originates from different error sources along the modeling chain. Hydrological impact studies have generally partitioned these error sources into global impact and global climate model (GIM and GCM, respectively) uncertainties, neglecting other sources, including scenarios and internal variability. Using a set of GIMs driven by GCMs under different representative concentration pathways (RCPs), this study aims to partition the uncertainty of future flows coming from GIMs, GCMs, RCPs, and internal variability over the CONterminous United States (CONUS). We focus on annual maximum, median, and minimum runoff, analyzed decadally over the twenty-first century. Results indicate that GCMs and GIMs are responsible for the largest fraction of uncertainty over most of the study area, followed by internal variability and to a smaller extent RCPs. To investigate the influence of the ensemble setup on uncertainty, in addition to the full ensemble, three ensemble configurations are studied using fewer GIMs (excluding least credible GIMs in runoff representation and GIMs accounting for vegetation and CO₂ dynamics), and excluding intermediate RCPs. Overall, the use of fewer GIMs has a minor impact on uncertainty for low and medium flows, but a substantial impact for high flows. Regardless of the number of pathways considered, RCPs always play a very small role, suggesting that improvement of GCMs and GIMs and more informed ensemble selections can yield a reduction of projected uncertainties.     

High-resolution climate simulation of the last glacial maximum

The climate of the last glacial maximum (LGM) is simulated with a high-resolution atmospheric general circulation model, the NCAR CCM3 at spectral truncation of T170, corresponding to a grid cell size of roughly 75 km. The purpose of the study is to assess whether there are significant benefits from the higher resolution simulation compared to the lower resolution simulation associated with the role of topography. The LGM simulations were forced with modified CLIMAP sea ice distribution and sea surface temperatures (SST) reduced by 1°C, ice sheet topography, reduced CO₂, and 21,000 BP orbital parameters. The high-resolution model captures modern climate reasonably well, in particular the distribution of heavy precipitation in the tropical Pacific. For the ice age case, surface temperature simulated by the high-resolution model agrees better with those of proxy estimates than does the low-resolution model. Despite the fact that tropical SSTs were only 2.1°C less than the control run, there are many lowland tropical land areas 4–6°C colder than present. Comparison of T170 model results with the best constrained proxy temperature estimates (noble gas concentrations in groundwater) now yield no significant differences between model and observations. There are also significant upland temperature changes in the best resolved tropical mountain belt (the Andes). We provisionally attribute this result in part as resulting from decreased lateral mixing between ocean and land in a model with more model grid cells. A longstanding model-data discrepancy therefore appears to be resolved without invoking any unusual model physics. The response of the Asian summer monsoon can also be more clearly linked to local geography in the high-resolution model than in the low-resolution model; this distinction should enable more confident validation of climate proxy data with the high-resolution model. Elsewhere, an inferred salinity increase in the subtropical North Atlantic may have significant implications for ocean circulation changes during the LGM. A large part of the Amazon and Congo Basins are simulated to be substantially drier in the ice age—consistent with many (but not all) paleo data. These results suggest that there are considerable benefits derived from high-resolution model regarding regional climate responses, and that observationalists can now compare their results with models that resolve geography at a resolution comparable to that which the proxy data represent.     

River discharge to the Baltic Sea in a future climate

This study reports on new projections of discharge to the Baltic Sea given possible realisations of future climate and uncertainties regarding these projections. A high-resolution, pan-Baltic application of the Hydrological Predictions for the Environment (HYPE) model was used to make transient simulations of discharge to the Baltic Sea for a mini-ensemble of climate projections representing two high emissions scenarios. The biases in precipitation and temperature adherent to climate models were adjusted using a Distribution Based Scaling (DBS) approach. As well as the climate projection uncertainty, this study considers uncertainties in the bias-correction and hydrological modelling. While the results indicate that the cumulative discharge to the Baltic Sea for 2071 to 2100, as compared to 1971 to 2000, is likely to increase, the uncertainties quantified from the hydrological model and the bias-correction method show that even with a state-of-the-art methodology, the combined uncertainties from the climate model, bias-correction and impact model make it difficult to draw conclusions about the magnitude of change. It is therefore urged that as well as climate model and scenario uncertainty, the uncertainties in the bias-correction methodology and the impact model are also taken into account when conducting climate change impact studies.     

The role of temperature on treeline migration for an eastern African mountain during the Last Glacial Maximum

Paleo-data suggest that East African mountain treelines underwent an altitudinal shift during the Last Glacial Maximum (LGM). Understanding the ecological and physiological processes underlying treeline response to such past climate change will help to improve forecasts of treeline change under future global warming. In spite of significant improvements in paleoclimatic reconstruction, the climatic conditions explaining this migration are still debated and important factors such as atmospheric CO₂ concentration, the impact of lapse rate decreasing temperature along altitudinal gradients and rainfall modifications due to elevation have often been neglected or simplified. Here, we assess the effects of these different factors and estimate the influence of the most dominant factors controlling changes in past treeline position using a multi-proxy approach based on simulations from BIOME4, a coupled biogeography and biogeochemistry model, modified to account for the effect of elevation on vegetation, compared with pollen, and isotopic data. The results indicate a shift in mountain vegetation at the LGM was controlled by low pCO₂ and low temperatures promoting species morphologically and physiologically better adapted to LGM conditions than many trees composing the forest belt limit. Our estimate that the LGM climate was cooler than today’s by ?4.5 °C (range: ?4.3 to ?4.6 °C) at the upper limit of the treeline, whereas at 831 m it was cooler by ?1.4 °C (range: ?2.6 to ?0.6 °C), suggests that a possible lapse rate modification strongly constrained the upper limit of treeline, which may limit its potential extension under future global warming.     

The role of ecosystem-atmosphere interactions in simulated Amazonian precipitation decrease and forest dieback under global climate warming

Summary A suite of simulations with the HadCM3LC coupled climate-carbon cycle model is used to examine the various forcings and feedbacks involved in the simulated precipitation decrease and forest dieback. Rising atmospheric CO₂ is found to contribute 20% to the precipitation reduction through the physiological forcing of stomatal closure, with 80% of the reduction being seen when stomatal closure was excluded and only radiative forcing by CO₂ was included. The forest dieback exerts two positive feedbacks on the precipitation reduction; a biogeophysical feedback through reduced forest cover suppressing local evaporative water recycling, and a biogeochemical feedback through the release of CO₂ contributing to an accelerated global warming. The precipitation reduction is enhanced by 20% by the biogeophysical feedback, and 5% by the carbon cycle feedback from the forest dieback. This analysis helps to explain why the Amazonian precipitation reduction simulated by HadCM3LC is more extreme than that simulated in other GCMs; in the fully-coupled, climate-carbon cycle simulation, approximately half of the precipitation reduction in Amazonia is attributable to a combination of physiological forcing and biogeophysical and global carbon cycle feedbacks, which are generally not included in other GCM simulations of future climate change. The analysis also demonstrates the potential contribution of regional-scale climate and ecosystem change to uncertainties in global CO₂ and climate change projections. Moreover, the importance of feedbacks suggests that a human-induced increase in forest vulnerability to climate change may have implications for regional and global scale climate sensitivity.     

Transient simulation of the last glacial inception. Part II: sensitivity and feedback analysis

The sensitivity of the last glacial-inception (around 115 kyr BP, 115,000 years before present) to different feedback mechanisms has been analysed by using the Earth system model of intermediate complexity CLIMBER-2. CLIMBER-2 includes dynamic modules of the atmosphere, ocean, terrestrial biosphere and inland ice, the last of which was added recently by utilising the three-dimensonal polythermal ice-sheet model SICOPOLIS. We performed a set of transient experiments starting at the middle of the Eemiam interglacial and ran the model for 26,000 years with time-dependent orbital forcing and observed changes in atmospheric CO₂ concentration (CO₂ forcing). The role of vegetation and ocean feedback, CO₂ forcing, mineral dust, thermohaline circulation and orbital insolation were closely investigated. In our model, glacial inception, as a bifurcation in the climate system, appears in nearly all sensitivity runs including a run with constant atmospheric CO₂ concentration of 280 ppmv, a typical interglacial value, and simulations with prescribed present-day sea-surface temperatures or vegetation cover—although the rate of the growth of ice-sheets growth is smaller than in the case of the fully interactive model. Only if we run the fully interactive model with constant present-day insolation and apply present-day CO₂ forcing does no glacial inception appear at all. This implies that, within our model, the orbital forcing alone is sufficient to trigger the interglacial–glacial transition, while vegetation, ocean and atmospheric CO₂ concentration only provide additional, although important, positive feedbacks. In addition, we found that possible reorganisations of the thermohaline circulation influence the distribution of inland ice.     

Effects of Greenhouse Gas Emissions on World Agriculture, Food Consumption, and Economic Welfare

Because of many uncertainties, quantitative estimates of agriculturally related economic impacts of greenhouse gas emissions are often given low confidence. A major source of uncertainty is our inability to accurately project future changes in economic activity, emissions, and climate. This paper focuses on two issues. First, to what extent do variable projections of climate generate uncertainty in agriculturally related economic impacts? Second, to what extent do agriculturally related economic impacts of greenhouse gas emissions depend on economic conditions at the time of impacts? Results indicate that uncertainty due to variable projections of climate is fairly large for most of the economic effects evaluated in this analysis. Results also indicate that economic conditions at the time of impact influence the direction and size of as well as the confidence in the economic effects of identical projections of greenhouse gas impacts. The economic variable that behaves most consistently in this analysis is world crop production. Increases in mean global temperature, for example, cause world crop production to decrease on average under both 1990 and improved economic conditions and in both instances the confidence with respect to variable projections of climate is medium (e.g.,67%) or greater. In addition and as expected, CO₂ fertilization causesworld crop production to increase on average under 1990 and improved economic conditions. These results suggest that crop production may be a fairly robust indicator of the potential impacts of greenhouse gas emissions.A somewhat unexpected finding is that improved economic conditions are not necessarily a panacea to potential greenhouse-gas-induced damages, particularly at the region level. In fact, in some regions, impacts of climate change or CO₂ fertilization that are beneficial undercurrent economic conditions may be detrimental under improved economic conditions (relative to the new economic base). Australia plus New Zealand suffer from this effect in this analysis because under improved economic conditions they are assumed to obtain a relatively large share of income from agricultural exports. When the climate-change and CO₂-fertilization scenariosin this analysis are also included, agricultural exports from Australia plus New Zealand decline on average. The resultant declines in agricultural income in Australia plus New Zealand are too large to be completely offset by rising incomes in other sectors. This indicates that regions that rely on agricultural exports for relatively large shares of their income may be vulnerable not only to direct climate-induced agricultural damages, but also to positive impacts induced by greenhouse gas emissions elsewhere.     

Effects of physical changes in the ocean on the atmospheric pCO2: glacial-interglacial cycles

Based on LGM experiments with an atmosphere–ocean general circulation model, we systematically investigated the effects of physical changes in the ocean and induced biological effects as well on the low atmospheric CO₂ concentration (pCO₂) at the last glacial maximum (LGM). Numerical experiments with an oceanic carbon-cycle model showed that pCO₂ was lowered by ~30 ppm in the LGM ocean. Most of the pCO₂ reduction was explained by the change in CO₂ solubility in the ocean due to lower sea surface temperature (SST) during the LGM. Moreover, we found that SST changes in the high-latitude Northern Atlantic could explain more than one-third of the overall change in pCO₂ induced by global SST change, suggesting an important feedback between the Laurentide ice sheet and pCO₂.