The uncertainties inherent in the climate scenarios lead to a high uncertainty in the simulation results; for example, sites that are prone to heat waves and droughts under current climatic conditions will experience losses in carbon stocks under the most ‘extreme’ assumptions in all sce- narios but not necessarily under ‘moderate’ or ‘medium’ conditions. This suggests that while the national signal (i.e., continued carbon sink capacity) is relatively robust, the sub-national (regional) signal may be much less trust- worthy. This uncertainty needs to be taken into account in ecosystem management strategies, as they are typically developed at spatial scales much below the national scale. Acknowledgments We would like to thank Paul Miller from the Ecosystem Modelling & Biodiversity Studies (EMBERS) group at Lund University for providing the source code of LPJ-GUESS, and Lorenz Walthert from the Forest Soils and Biogeochemistry Unit of the Swiss Federal Institute for Forest Snow and Landscape Research for providing the climate and soil data. We are grateful to the Center for Climate Systems Modeling (C2SM) at ETH Zurich for providing the climatechange scenarios. We also like to thank two anonymous reviewers for their constructive comments. This study was funded by the Swiss National Competence Center in Research ‘Climate.’
The use of species occurrence data and climate model variables in this study potentially present uncertainty in interpretation of the results. Dataset acquired from observations and herbarium often shows strong geographic bias (sampling bias) due to some areas being visited more often than others because of their ac- cessibility  . The availability of presence distribution data for species poses challenge as most of herbaria in the region lack sufficient data for model- ling . Such challenges has however been overcome by use of cross-validation in maxentmodelling technique which uses few data points. Species occurrence data can be biasedly distributed on the landscape which can contribute to local biasness and over-smoothing affecting reliability of the model  . We, how- ever, rely on maxent ability in using jackknifing to achieve a robust new estima- tor, called jackknife kriging, which retains ordinary kriging simplicity and global unbiasedness while at the same time reducing local bias and over-smoothing tendency . The climate model consist of potentially highly correlated va- riables; however, maxent can only select one of variables in a pair of highly cor- related variables and still model performance is not affected  . However, the selection of variables by maxent is associated with the risk of diminishing importance of other predictor in the pairs of variables  . It is also impor- tant to note that there are confounding factors that may affect the distribution of agroforestry tree species on the slopes of Mount Kilimanjaro and Taita Hills. These factors include but not limited to soil, market forces and land use plan and management.
In this paper, I survey the estimates of the total economic impacts of climatechange. While climatechange is initially positive, incremental impacts are negative for any warming that can be avoided. Climatechange is therefore a negative externality. Published estimates suggest that climatechange is not a particularly large problem. However, estimates are available for a global warming of up to 3.0ºC while actual warming may well be (much) larger than that. I combine the primary estimates to form probability density functions for the impact of 1.0 ºC, 2.5ºC and 3.0ºC of warming. I use vote-counting rather than Bayesian updating so as to preserve the wide range of uncertainty. The primary suggests that the impacts follow a parabola and the PDFs are used to derive the probability distribution of the parabola’s parameters. This is in turn used for extrapolation. Impacts of warming of 3.0ºC or more are most likely negative. Beyond 7.0ºC, there is a reasonable chance that climatechange would imply a total loss of welfare – although the PDF is very diffuse. The uncertainty about the social cost of carbon is fairly wide too and grows over time. Although it cannot be excluded that greenhouse gas emissions should be subsidized, the expected value of the social cost of carbon points to a carbon tax that starts around $30/tC and rises at 2% per year.
There are a number of limitations to our approach which must be taken into account for improving future studies. Total NPP, especially the allocation of biomass below- ground, is difficult to quantify, especially soluble carbon inputs as root exudations. The influence of nitrogen and phosphorus also needs to be factored into NPP estimates, however this is model dependent. In this study we have used a minimum dataset approach, to demonstrate the utility of simple models in examining the impact of cli- mate change on terrestrial carbon cycling. In our simula- tions, we have also assumed that land use does not change in response to climatechange Projecting future land-use change is another component which could have as great an impact on continental SOC storageclimate and CO 2 changes. For increased accuracy, successional change algo- rithms should also be included into our models as species composition has a major bearing on nutrient turnover. Spatial variability is also a major problem and soil survey data available for Australia is problematic considering the size and geographic diversity of the country. Surveying is an expensive exercise but with the on-going advances in geostatistical and interpolation theory, and remote sens- ing, the accuracy in soil maps may be greatly improved at the landscape and even finer scales.
DYNAMIC PROJECTION OF CLIMATECHANGE SCENARIOS ON ABOVEGROUND CARBONSTORAGE OF TROPICAL TREES IN WEST PAPUA, INDONESIA. Through photosynthetic activities, tropical forest ecosystems capture and store the most significant carbon emissions in the form of biomass compared with other types of vegetation, and thus play a highly crucial part in dealing with climatechange. However, such important role of tropical forest is very fragile from extreme changes in temperature and precipitation, because carbonstorage in forest landscape is strongly related to those climate variables. This paper examines the impacts of future climate disturbances on aboveground carbonstorage of three tropical tree species, namely Myristica sp., Palaquium sp., and Syzygium sp. through “what if ” scenarios evaluation using Structural Thinking and Experimental Learning Laboratory with Animation (STELLA). Results highlighted that when the dynamic simulation was running with five IPCC’s climatechange scenarios (Constant year 2000 concentrations, B1, A1T, A2, and A1F1) for 200 years simulation period, then moderate climatechange scenarios occured, such as B1 and A1T, would have already caused significant statistical deviation to all of those tree species. At the worst level of A1F1, the 4°C temperature was coupled with 20% reduction in precipitation. Palaquium sp. showed the highest reduction of aboveground carbonstorage with about 17.216% below its normal value. This finding implies the negative climate feedbacks should be considered seriously to ensure the accuracy of long term forest carbon accounting under future climateuncertainty. Keywords: Climatechange, aboveground carbonstorage, West Papua, STELLA
The study has provided the present carbonstorage measurement of nine soil se- ries under major peatland of Bangladesh from 0 to 100 cm depth from the sur- face. Presently, about 125.02 kha land contains 0.12 Pg carbon stock in Bangla- desh whereas the amount was 0.25 Pg in the 70s. So, in the last five decades, it has almost lost 50% of carbon stock in a country like Bangladesh. It can also as- sume that other tropical and subtropical regions also face emission of huge car- bon over a long period due to low or no management that leads to rapid global warming and climate changes. Additionally, in peat soils, there has a limited negative correlation (r = −0.65) found between bulk density and soil organic carbon percentage. So, bulk density is one of the important factors for storing soil organic carbon in peat soil. The bulk density should be controlled for storing and management of soil organic carbon. Furthermore, it has also observed that peat soils are used as agricultural land almost all over the world that leads to high release to organic carbon in the atmosphere. These phenomena lead rapid mineralization of organic carbon from the soil. So, it should be minimized to control climatechange. Possible management option of peat soils is also dis- cussed briefly. It should include proper characterization, estimation, assessment, and regulation over the time to maintain sustainable environment.
At the heart of much of the Program’s work lies MIT’s Integrated Global System Model. Through this integrated model, the Program seeks to: discover new interactions among natural and human climate system components; objectively assess uncertainty in economic and climate projections; critically and quantitatively analyze environmental management and policy proposals; understand complex connections among the many forces that will shape our future; and improve methods to model, monitor and verify greenhouse gas emissions and climatic impacts. This reprint is one of a series intended to communicate research results and improve public understanding of global environment and energy challenges, thereby contributing to informed debate about climatechange and the economic and social implications of policy alternatives.
binations in a way that aims to explore the full range of projected climate impacts. Pre- vious studies have recommended using this method by calculating the maximum and minimum annual average changes in precipitation and temperature fields between a baseline and historical period for a set of GCMs    . In this approach it is assumed that “extreme” combinations of projected changes in climate variables will generate the full range of projected climate impacts, however it many studies this is highly unlikely. In order to overcome this problem, studies have used simplified impact models to directly assess the sensitivity of changes in climate on specific impacts. Ntegeka et al.  used a simple lumped hydrologic model to select climate scenarios likely to generate extreme river flows. It was found that in order to replicate the upper and lower bounds of flow, combinations of climate perturbations from different models for both precipitation and evapotranspiration were needed. Vano et al.  used a Vari- able Infiltration Capacity model and dynamic general vegetation model to test the sen- sitivities of seasonally-averaged streamflow and vegetation carbon respectively, to changes in annual temperature and precipitation. Kay et al.  also used a sensitivity based method to estimate flood peaks for a range of river basins in the UK by generat- ing a response surface dependent on the harmonic mean and amplitude of precipitation. The sensitivity metrics appear promising for quick selection of models to bound uncer- tainty with respect to extreme impacts. However, simplified impact models may not be able to capture the range of climate impacts projected by the full impact model, partic- ularly in the case of extreme hydrologic events.
In the AOV(LGM), there are some typical changes in the veg- etation pattern compared to the AOV(PI) (Fig. 1a and b). We first discuss changes in vegetation as determined according to the MATSIRO classification. In the south of the Scandi- navian and Laurentide ice sheets, boreal forests shift south- ward and tundra appears. In eastern Siberia, boreal forests retreat southward and the southern boundary of tundra is re- located to 50 N. The southern boundary of the boreal forests also shifts southward in North America and China. In the downstream region of the Scandinavian ice sheet, a slight northward shift of boreal forest appears. Total reduction of the area of boreal forest is 14.0 × 10 12 m 2 and expansion of tundra is 6.2 × 10 12 m 2 . Expansion of desert is seen in Central Asia, North and South Africa, and North America, totaling 15.1 ×10 12 m 2 of increase. In the tropical region, the tropical forest shrinks, and savanna or grassland appears or increases, especially in Africa. Tropical forest covers the exposed con- tinental shelf over the maritime continent. The continental shelf of the East China Sea to south of the Japan Sea is cov- ered by temperate forest. The East Siberian Sea and north of the Bering Sea turns into tundra. According to LPJ-DGVM PFT classification, a fraction of grass PFTs in LPJ-DGVM increase in high latitude (Fig. 1c) and decreases in the cen- tral Eurasia. A fraction of forest shows a decrease in both northern high latitudes and tropical regions (Fig. 1d). The NPP also shows a drastic change (Fig. 3a and h). The global total annual NPP during the LGM and the preindustrial are 44.0 PgC yr −1 and 59.5 PgC yr −1 , respectively (Table 3). Re- duction of 5.4 PgC yr −1 occurs due to covering by the ex- panded ice sheet, and an increase of 7.5 PgC yr −1 occurs on the exposed continental shelf due to a change in sea level (these values are not shown in the table). In the AOV(LGM), a large part of the total reduction of NPP is seen in the forest region (see Fig. 3h) compared to AOV(PI).
Manuscript received: 11 August 2012. Revision accepted: 17 October 2012.
Srivastava AK, Rai MK. 2012. Review: Sugarcane production: impact of climatechange and its mitigation 13: 214-227. Sugarcane is a climatic sensitive crop: therefore, its spatial distribution on the globe is restricted as per the suitability of various climatic parameters. The climatechange, though, a very slow phenomenon is now accelerated due to natural, as well as enormous human activities disturbing the composition of atmosphere. The predications of various climatic models for probable rise in temperature, rainfall, sea level show an alarming condition in forthcoming decades. As the sugarcane is very sensitive to temperature, rainfall, solar radiations etc. therefore, a significant effect on its production and sugar yield is expected in future. It is also well known that sugarcane is one of the precious crops of the world and its end products i.e. sugar and ethanol have a continuous growing demand on global level. Hence, the studies related to good production of sugarcane in changing conditions of climate has become one among the front line area of research and is a major concern of scientist’s world over. Advance agronomic measures including development of suitable cane varieties susceptible to changed climatic conditions, land preparation, time and pattern of plantation, weed, disease and pest managements, nutrients managements, proper timing and adequate water management seems to be the affective measures for obtaining high production of crop with good quality juice in future.
Abstract : The Sundarban is a vast forest in the coastal region of the Bay of Bengal which is one of the natural wonders of the world. The present paper is an attempt to analyse the climatechange and itsimpact on biological diversity of Indian Sundarban. The total area of Indian Sundarban is about 9360 Sq.km comprising of 102 Island. In 1997, Sundarban was recognized as UNESCO world Heritage site. The highly specialized mangrove ecosystem supports rich biodiversity. Many floral and faunal species are highly endangered due to climatic changes and people activities. In terms of biodiversity, Sundarbans serves as an important refuge for several endangered and threatened mammals including the tiger, smooth coated otter, and great Indian Civet. The region also has several smaller predators such as jungle cat, fishing cat, and leopard. Continued emission of greenhouse gases will cause further warming and long lasting changes in all components of the climate system, increasing the likelihood of severe, pervasive and irreversible impacts for people and ecosystem.
It is important to note that the analysis here is based on the assumption that land use patterns have adjusted completely to climatechange. This issue is discussed further by Mendelsohn, Nordhaus and Shaw (1994, 1999). As Quiggin and Horowitz (1999, 2003) observe, a large proportion of the costs of global climatechange consists of adjustment costs. As long as the climate continues to change, adjustment costs will continue. Hence, the analysis presented here may be regarded as representing the impact of a policy under which global temperatures are stabilised at a new and higher level.
Uncertainty in downscaling future climate projections due to weather generators. For each climate index, the proximity of different GCM × RCP combinations was assessed by approximating the area of overlap of the probability density distributions of the index values obtained with each WG. The results summa- rized in Fig. 5a,b return a quite high likelihood (0.7) to obtain the same value for different indices regardless of the GCM × RCP combination. This likelihood was indeed higher than the average resemblance of generated to refer- ence baselines (<0.5). While a strong dependence of results on geographical location (site-specific) was somewhat expected, the most interesting outcome (Fig. 5b) was that results from different GCMs and RCPs tended to cluster by WG (more than by site). Given the influence of the different WGs on climate scenarios, the additional uncer- tainty that may result from the interaction between generation (WGs) and projections (GCMs and RCPs) was ana- lyzed. A series of nonparametric robust rank order ANOVA tests was thus conducted to assess the effect of different factors (Site, WG, GCM, RCP, and their interactions) on each climate index calculated on future series (Fig. 6).
observed relationships, dynamic process-based models for par- ticular crop types, and generalized large and field scale process- based models. Crop models vary in their complexity, how they simulate dynamic processes (e.g., crop development), and which processes they simulate (e.g., high temperature stress around anthesis and/or microclimate). Crop models based on observed relationships have set parameters for a given cultivar (“genetic coefficients”), determined through field experiments; thus not accounting for parametric uncertainty. Regional scale crop mod- els may be optimized to observed yield data and a parameter ensemble may be used (Challinor et al., 2009a). Impact model studies are limited by the number of observable output variables that can be used for parameterization, regardless of the approach taken and the application.
Complexity and confusion at the level of practice generates the uncertainty necessary for criminal climate fraud. The year 2009 witnessed an explosion of fraud in the carbon markets surrounding three basic strategies. Tax evasion or so-called carousel fraud marked the largest scam in monetary terms. Europol estimated value added tax fraud at about $7.4 billion in lost revenue (The Guardian, 2009). Perhaps most spectacularly, the Hungarian government found a way to recycle emissions permits that had already been used for European compliance obligations, only for brokers to discover these same permits had resurfaced within European exchanges. Prices collapsed when the news broke and while it turned out that the trades were technically legal, the experience revealed both that Japan was actively allowing companies to submit recycled permits for their voluntary obligations and, more importantly, the European rules allowed for a lucrative form of arbitrage whereby the more valuable EUAs (EU emission allowances) could be recycled provided an equivalent number of the national units, AAUs, were taken out of circulation. Lastly, a phishing scam in which registry accounts were hacked and allowances stolen, then sold back into the market, created a situation in which buyers had no way of knowing whether they held stolen property in their accounts. The spot market closed for three weeks to accommodate the situation, while the European Commission created a new directive such that the account holder of a carbon permit would be held to be the legal owner regardless of the means by which the permits were obtained (European Commission, 2011). Such strategies involve cunning repurposing of the rules or technical opportunities to be exploited. The world’s largest carbon finance institution, Barclays Capital, has called for tighter regulation of European spot markets in carbon because the fraud ‘feed[s] suspicions about the reliability’ of those markets (Carbon Finance, 2010b: 9). This is probably not what RWE climate officer Ludwig Kons meant when he said that carbon markets had ‘awakened the world’s technical imagination’ (Klawitter 2010). As Friends of the Earth (2010) suggests, markets, too, are technical devices subject to ingenious play.
1 Swiss Federal Research Institute WSL, Zu¨cherstrasse 111, 8903 Birmensdorf, Switzerland; 2 Research Station Agroscope Reckenholz- Ta¨nikon ART, Reckenholzstrasse 191, 8046 Zurich, Switzerland
We assessed how consequences of future land-use change may affect size and spatial shifts of C stocks under three potential trends in policy—(a) busi- ness-as-usual: continuation of land-use trends ob- served during the past 15 years; (b) extensification: full extensification of open-land; and (c) liberal- ization: full reforestation potential. The build-up times for the three scenarios are estimated at 30, 80 and 100 years, respectively. Potential C-stock change rates are derived from the literature. Whereas the business-as-usual scenario would cause marginal changes of 0.5%, liberalization would provoke a 13% increase in C stocks (+62 MtC). Gains of 24% would be expected for forests (+95 MtC), whereas open-land C stock would decrease 27% (-33 MtC). Extensification would lead to a C stock decrease of 3% (-12 MtC). Whereas forest C is expected to increase 12% (+36.5 MtC) at high elevations, stocks of open-land
Even ignoring market failures other than those directly associated with carbon emissions, one may question the conventional wisdom that an carbon tax will give an e¢ cient mitigation of carbon emissions. The conventional wisdom is based on the ability of governments to commit to a future tax path. In reality, this is not possible. Without commitment, market agents who make investment decisions must base their decisions on what they ex- pect about future climate policies. In a hypothetical world without any un- certainty related to technology, preferences, etc. in the future, future carbon taxes could be correctly predicted even in the absence of commitment. In the real world these conditions obviously do not hold. As discussed in more detail in section 6, non-commitment can therefore lead to uncertain and/or wrong predictions about the future carbon tax.
The social cost of carbon (SCC) is an estimate of the economic damages from one metric ton of carbon dioxide emissions. Estimates of SCC for use in U.S. policy- making were developed by an interagency workgroup in 2009-10 (Greenstone, Kopits and Wolverton, 2013). The SCC for 2015 ranges from $12 to $61 depending on the discount rate. SCC was developed using the average of estimates from three integrated assessment models and includes impacts from the agricultural, health, and real estate sectors of the economy. Only one of the three models, the Dynamic Integrated Model for Climate and the Economy (DICE), includes nonmarket impacts. In revising the SCC estimates developed in 2009-10, the U.S. Environmental Protection Agency and U.S. Department of Energy hosted a workshop in 2011 to improve and update the SCC. One conclusion was “that there is very little existing research with which to develop the SCC for marine resources” (ICF International, 2011), including recreation.