Using a GCM analogue model to investigate the potential for Amazonian forest dieback

Summary A combined GCM analogue model and GCM land surface representation is used to investigate the influences of climatology and land surface parameterisation on modelled Amazonian vegetation change. This modelling structure (called IMOGEN) captures the main features of the changes in surface climate as estimated by a GCM with enhanced atmospheric greenhouse gas concentrations. Advantage is taken of IMOGENs computational speed which allows multiple simulations to be carried out to assess the robustness of the GCM results.The timing of forest dieback is found to be sensitive to the initial pre-industrial climate, as well as uncertainties in the representation of land-atmosphere CO₂ exchange. Changing from a Q ₁₀ form for plant dark and maintanence respiration (as used in the coupled GCM runs) to a respiration proportional to maximum photosynthesis, reduces the biomass lost from Amazonia in the 21st century. Replacing the GCM control climate (which has about 25% too little rain in the annual mean over Amazonia) with an observed climatology increases the CO₂ concentration at which rainfall drops to critical levels, and thereby further delays the dieback. On the other hand, calibration of the canopy photosynthesis model against Amazonian flux data tends to lead to earlier forest dieback. Further advances are required in both GCM rainfall simulation and land-surface process representation before a clearer picture will emerge on the timing of possible Amazonian forest dieback. However, it seems likely that these advances will overall lead to projections of later forest dieback as GCM control climates become more realistic.     

Effects of Amazonian deforestation on climate: a numerical experiment with a coupled biosphere-atmosphere model with soil hydrology

Summary A coupled biosphere-atmosphere statistical-dynamical model (SDM) is used to study the climatic effects of Amazonian deforestation. A soil moisture model based on BATS has been incorporated into the SDM in order to study the biogeophysical feedback of change in surface characteristics to regional climate due to the deforestation. In the control experiment, the mean annual and mean seasonal climate is well simulated by the model when compared with NCEP/NCAR reanalysis data. In the deforestation experiment, the evergreen broadleaf trees in the Amazonian region are substituted by short grass. The effects of Amazonian deforestation on regional climate are analysed taking into account the model simulations for the land portion of the latitude belts comprising the tropical region. Amazonian deforestation results in regional climate changes such as a decrease in evaporation, precipitation, available surface net radiation and soil moisture content, and an increase in temperatures and sensible heat flux. The reduction in transpiration was responsible for the most part of the decrease in total evapotranspiration. The reduction in precipitation was larger than the decrease in evapotranspiration so that runoff was reduced. The simulation of the diurnal cycle of the surface temperature shows an increase in temperature during the day and a decrease at night, which is in agreement with observations, whereas earlier GCM experiments showed an increase both during the day and night. In general, the changes in temperature and energy fluxes are in good agreement with GCM experiments, showing that the SDM is able to simulate the characteristics of the tropical climate that are associated with the substitution of forest by pasture areas.     

Comparison of uncertainty sources for climate change impacts: flood frequency in England

This paper investigates the uncertainty in the impact of climate change on flood frequency in England, through the use of continuous simulation of river flows. Six different sources of uncertainty are discussed: future greenhouse gas emissions; Global Climate Model (GCM) structure; downscaling from GCMs (including Regional Climate Model structure); hydrological model structure; hydrological model parameters and the internal variability of the climate system (sampled by applying different GCM initial conditions). These sources of uncertainty are demonstrated (separately) for two example catchments in England, by propagation through to flood frequency impact. The results suggest that uncertainty from GCM structure is by far the largest source of uncertainty. However, this is due to the extremely large increases in winter rainfall predicted by one of the five GCMs used. Other sources of uncertainty become more significant if the results from this GCM are omitted, although uncertainty from sources relating to modelling of the future climate is generally still larger than that relating to emissions or hydrological modelling. It is also shown that understanding current and future natural variability is critical in assessing the importance of climate change impacts on hydrology.     

Incorporating ‘catastrophic’ climate change into policy analysis

Although existing economic research is informative with regard to the importance of including potential ‘catastrophic’ climate change impacts in the analysis of GHG mitigation benefits, the generic and abstract form of the ‘catastrophe’ implemented has led to a lack of specific policy implications. This article provides an important starting point for a discussion of how to improve economic modelling of potential large-scale impacts of climate change. It considers how the term ‘abrupt climate change’ has been used in the scientific literature to describe changes in the climate system and carefully reviews the characteristics of the events that have been discussed in this context. The findings are compared to the way in which the economic literature has modelled potential economic and human welfare impacts of these ‘catastrophic’ events. In general, the economics literature is found to have modelled such impacts in a uniform way that fails to account for differences in relevant end points and timescales. The result is policy recommendations based on events that do not resemble those of concern. Better treatment of these events in integrated assessment modelling would help ensure that future research efforts can serve as meaningful policy input.     

Sensitivity Studies of the Impacts of Climate Change on White Nile Flows

Several exploratory studies are presented on the sensitivity of the water balance of the White Nile to climate change, using both observed and stochastic time series to drive the models. Example results are presented using various assumed climate change scenarios and results from a General Circulation Model (GCM). The relative merits and shortcomings of each modelling approach are also discussed. A simple analytical model for Lake Victoria is also used to illustrate some of the overall features of the lake's likely response. Particular difficulties with the White Nile system are that, due to the huge area of open water in the basin, transient responses to short-lived events can occur over timescales comparable with those for which long term climate change impacts are being studied, and predicted changes in flows are extremely sensitive to estimates for the rainfall and evaporation at lake and swamp surfaces. Of the modelling approaches considered, the network simulation approach with stochastic inputs is recommended as a way of smoothing out these transient effects, and assessing the uncertainty in the results due to inaccuracies in the data, the model parameters and the climate change predictions. The paper concludes with a brief discussion of some other areas of uncertainty in the hydrological modelling of White Nile flows and possible alternative external forcing mechanisms for flows in the next few decades.     

The atmospheric boundary layer — advances in knowledge and application

We summarise major activities and advances in boundary-layer knowledge in the 25 years since 1970, with emphasis on the application of this knowledge to surface and boundary-layer parametrisation schemes in numerical models of the atmosphere. Progress in three areas is discussed: (i) the mesoscale modelling of selected phenomena; (ii) numerical weather prediction; and (iii) climate simulations. Future trends are identified, including the incorporation into models of advanced cloud schemes and interactive canopy schemes, and the nesting of high resolution boundary-layer schemes in global climate models.     

Uncertainty, ignorance and ambiguity in crop modelling for African agricultural adaptation

Drawing on social constructivist approaches to interpreting the generation of knowledge, particularly Stirling’s (Local Environ 4(2):111–135, 1999) schema of incomplete knowledge, this paper looks critically at climate-crop modelling, a research discipline of growing importance within African agricultural adaptation policy. A combination of interviews with climate and crop modellers, a meta-analysis survey of crop modelling conducted as part of the CGIAR’s Climate Change Agriculture and Food Security (CCAFS) programme in 2010, and peer-reviewed crop and climate modelling literature are analysed. Using case studies from across the crop model production chain as illustrations it is argued that, whilst increases in investment and growth of the modelling endeavour are undoubtedly improving observational data and reducing ignorance, the future of agriculture remains uncertain and ambiguous. The expansion of methodological options, assumptions about system dynamics, and divergence in model outcomes is increasing the space and need for more deliberative approaches to modelling and policy making. Participatory and deliberative approaches to science-policy are advanced in response. The discussion highlights the problem that, uncertainty and ambiguity become hidden within the growing complexity of conventional climate and crop modelling science, as such, achieving the transparency and accessibility required to democratise climate impact assessments represents a significant challenge. Suggestions are made about how these challenges might be responded to within the climate-crop modelling community.     

Transient climatic response to an increase of greenhouse gases

The physical factors governing the transient climatic response to an increase of greenhouse gases are discussed, reasons for remaining uncertainties are identified, and recent climate modelling results are briefly summarized. The relevance of the transient response, and of uncertainties in the transient response, to questions such as the applicability of equilibrium climate model simulations to a gradual greenhouse gas increase, the verification of model projections, rates of climatic change, and the impacts of preventative strategies for dealing with the buildup of greenhouse gases is also discussed.     

Global models meet global policy: How can global and regional modellers connect with environmental policy makers? What has hindered them? What has helped?

Global modelling has developed over the last two decades largely because of the needs of policymaking, yet it has not yet realized its full potential in assisting policymaking. While the first wave of global models in the 1970s had a clear influence on policy discourse, these models were eventually undermined by attacks on their scientific underpinning. The second wave of global models in the 1990s had a stronger scientific footing than the first wave because they were built on a much larger base of knowledge about the global system. Although the improved scientific credibility of global models has increased their use in policymaking, a more subtle yet important factor has been the direct contact of global model developers with policymakers. Taking an example from the field of climate policy, a global modelling team has conducted formal workshops with a group of international climate policymakers with the aim to increase the relevance of modeling activities to policy issues. This interaction has inspired policyrelevant uses of a global model (IMAGE 2) and spawned the development of a new policy concept ‘Safe Emission Corridors’, which has been discussed in the frame of international climate negotiations. A preliminary conclusion is that both improved scientific credibility and improved interaction between modelers and policymakers are critical ingredients for enhancing the policy-relevance of global models.     

A Regional, Multi-sectoral And Integrated Assessment Of The Impacts Of Climate And Socio-economic Change In The Uk

Policy makers and stakeholders are increasingly demanding impact assessments which produce policy-relevant guidance on the local impacts of global climate change. The ‘Regional Climate Change Impact and Response Studies in East Anglia and North West England’ (RegIS) study developed a methodology for stakeholder-led, regional climate change impact assessment that explicitly evaluated local and regional (sub-national) scale impacts and adaptation options, and cross-sectoral interactions between four major sectors driving landscape change (agriculture, biodiversity, coasts and floodplains and water resources). The ‘Drivers-Pressure-State-Impact-Response’ (DPSIR) approach provided a structure for linking the modelling and scenario techniques. A 5 × 5 km grid was chosen for numerical modelling input (climate and socio-economic scenarios) and output, as a compromise between the climate scenario resolution (10 × 10 km) and the detailed spatial resolution output desired by stakeholders. Fundamental methodological issues have been raised by RegIS which reflect the difficulty of multi-sectoral modelling studies at local scales. In particular, the role of scenarios, error propagation in linked models, model validity, transparency and transportability as well as the use of integrated assessment to evaluate adaptation options to climate change are examined. Integrated assessments will provide new insights which will compliment those derived by more detailed sectoral assessments.     

二十世纪九十年代区域气候模拟研究进展

由于区域气候异常与社会经济及人类发展有紧密联系,因而长期以来受到各国气候学家的极大重视,与此密切相关的对区域气候的模拟研究也有了较大发展。进入２０世纪９０年代以来,区域气候模拟研究的进展更加明显。文中将着重分析全球气候模式对区域气候模拟的可靠性与不确定性;国内外对区域气候的模拟研究进展;以及对区域气候变化模拟研究的展望。     

Comparing precipitation bias correction methods for high-resolution regional climate simulations using COSMO-CLM

A new parametric bias correction method for precipitation with an extension for extreme values is compared to an empirical and an existing parametric method. The bias corrections are applied to the regional climate model COSMO-CLM (consortium for small-scale modelling – climate limited area modelling) with a resolution of 4.5 km for the time periods 1991–2000 and 2091–2100. In addition to a comparison in a cross-validation framework, a focus is laid on the investigation of extreme value correction and the effect of the bias correction on the climate change signal. According to the statistical methods used in this study, it was found that the empirical method outperforms both parametric alternatives. However, due to the limited length of the available time series, some outliers occurred, and all methods had problems correcting extreme values. The climate change signal is moderately influenced by all three methods, and the power of climate change detection is reduced. The largest effect was found for the number of dry days and the mean daily intensity, which are considerably altered after correction.     

Validation of model improvements for the GISS GCM

The general circulation model of the NASA/Goddard Institute for Space Studies (GISS GCM) was designed primarily for global climate change and climate sensitivity applications. The modelling group at GISS has developed new and more physically appropriate parameterizations of meteorological/hydrological processes which are being validated in an effort to improve the performance of the Model II version of the GISS GCM. This study discusses some preliminary evaluations of this testing based on multiple-year simulations at 4° latitude by 5° longitude horizontal resolution. These runs individually incorporate new formulations of the planetary boundary layer (PBL), the moist cumulus convection scheme and the ground hydrology and compare results using B-grid and C-grid numerics. The new PBL produces a realistically stronger tropical surface circulation, while the new cumulus scheme generates more realistic distributions of tropical convection and moisture. The main impact of the more sophisticated ground hydrology model is to increase surface air temperatures. Improvements in modelled sea level pressure and rainfall features by the C-grid are somewhat offset by increases in speed excesses at the cores of the summer hemisphere westerly jets. Each modelling innovation targeted a different aspect of the climate not adequately represented by Model II. However, since the various modelling changes were tested individually, the present evaluation could not demonstrate many dramatic improvements in the simulated climates. This documentation of impacts should, however, serve as a benchmark for the validation of future simulations of the GISS GCM that combine all of the modelling improvements.     

An annotated bibliography on the greenhouse effect and climate change

The literature on climate change from an enhanced greenhouse effect is large and growing rapidly. The problems considered are increasingly inter-disciplinary. For these reasons many workers will find useful pointers to the literature in the fields interacting with, but outside of, their own. We present here an annotated bibliography on issues relating to changes in the concentrations of Earth's greenhouse gases. The areas covered include theory and numerical modelling of climate change; cycles involving carbon dioxide and other radiatively important trace gases; observations of climate change and the problems associated with those observations; paleoclimatology as it relates to previous changes in the greenhouse gases; the impacts on and interactions with managed and natural ecosystems from climate change; policy issues related to climate change and to the limitation of climate change; history of the study of the greenhouse effect; and some other causes of climate change. Selection of papers has been made to facilitate rapid introduction to most of the important issues and findings in an area. Over 600 articles, reports, and books are discussed.     

Projection of future climate change conditions using IPCC simulations, neural networks and Bayesian statistics. Part 2: Precipitation mean state and seasonal cycle in South America

Evaluating the response of climate to greenhouse gas forcing is a major objective of the climate community, and the use of large ensemble of simulations is considered as a significant step toward that goal. The present paper thus discusses a new methodology based on neural network to mix ensemble of climate model simulations. Our analysis consists of one simulation of seven Atmosphere–Ocean Global Climate Models, which participated in the IPCC Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three SRES scenarios: A2, A1B and B1. Our statistical method based on neural networks and Bayesian statistics computes a transfer function between models and observations. Such a transfer function was then used to project future conditions and to derive what we would call the optimal ensemble combination for twenty-first century climate change projections. Our approach is therefore based on one statement and one hypothesis. The statement is that an optimal ensemble projection should be built by giving larger weights to models, which have more skill in representing present climate conditions. The hypothesis is that our method based on neural network is actually weighting the models that way. While the statement is actually an open question, which answer may vary according to the region or climate signal under study, our results demonstrate that the neural network approach indeed allows to weighting models according to their skills. As such, our method is an improvement of existing Bayesian methods developed to mix ensembles of simulations. However, the general low skill of climate models in simulating precipitation mean climatology implies that the final projection maps (whatever the method used to compute them) may significantly change in the future as models improve. Therefore, the projection results for late twenty-first century conditions are presented as possible projections based on the “state-of-the-art” of present climate modeling. First, various criteria were computed making it possible to evaluate the models’ skills in simulating late twentieth century precipitation over continental areas as well as their divergence in projecting climate change conditions. Despite the relatively poor skill of most of the climate models in simulating present-day large scale precipitation patterns, we identified two types of models: the climate models with moderate-to-normal (i.e., close to observations) precipitation amplitudes over the Amazonian basin; and the climate models with a low precipitation in that region and too high a precipitation on the equatorial Pacific coast. Under SRES A2 greenhouse gas forcing, the neural network simulates an increase in precipitation over the La Plata basin coherent with the mean model ensemble projection. Over the Amazonian basin, a decrease in precipitation is projected. However, the models strongly diverge, and the neural network was found to give more weight to models, which better simulate present-day climate conditions. In the southern tip of the continent, the models poorly simulate present-day climate. However, they display a fairly good convergence when simulating climate change response with a weak increase south of 45°S and a decrease in Chile between 30 and 45°S. Other scenarios (A1B and B1) strongly resemble the SRES A2 trends but with weaker amplitudes.     

A framework for testing the ability of models to project climate change and its impacts

Models used for climate change impact projections are typically not tested for simulation beyond current climate conditions. Since we have no data truly reflecting future conditions, a key challenge in this respect is to rigorously test models using proxies of future conditions. This paper presents a validation framework and guiding principles applicable across earth science disciplines for testing the capability of models to project future climate change and its impacts. Model test schemes comprising split-sample tests, differential split-sample tests and proxy site tests are discussed in relation to their application for projections by use of single models, ensemble modelling and space-time-substitution and in relation to use of different data from historical time series, paleo data and controlled experiments. We recommend that differential-split sample tests should be performed with best available proxy data in order to build further confidence in model projections.     

Naturally forced multidecadal variability of the Atlantic meridional overturning circulation

The mechanisms by which natural forcing factors alone could drive simulated multidecadal variability in the Atlantic meridional overturning circulation (AMOC) are assessed in an ensemble of climate model simulations. It is shown for a new state-of-the-art general circulation model, HadGEM2-ES, that the most important of these natural forcings, in terms of the multidecadal response of the AMOC, is solar rather than volcanic forcing. AMOC strengthening occurs through a densification of the North Atlantic, driven by anomalous surface freshwater fluxes due to increased evaporation. These are related to persistent North Atlantic atmospheric circulation anomalies, driven by forced changes in the stratosphere, associated with anomalously weak solar irradiance during the late nineteenth and early twentieth centuries. Within a period of approximately 100 years the 11-year smoothed ensemble mean AMOC strengthens by 1.5 Sv and subsequently weakens by 1.9 Sv, representing respectively approximately 3 and 4 standard deviations of the 11-year smoothed control simulation. The solar-induced variability of the AMOC has various relevant climate impacts, such as a northward shift of the intertropical convergence zone, anomalous Amazonian rainfall, and a sustained increase in European temperatures. While this model has only a partial representation of the atmospheric response to solar variability, these results demonstrate the potential for solar variability to have a multidecadal impact on North Atlantic climate.     

Top-down and bottom-up modelling to support low-carbon scenarios: climate policy implications

Bottom-up and top-down models are used to support climate policies, to identify the options required to meet GHG abatement targets and to evaluate their economic impact. Some studies have shown that the GHG mitigation options provided by economic top-down and technological bottom-up models tend to vary. One reason for this is that these models tend to use different baseline scenarios. The bottom-up TIMES_PT and the top-down computable general equilibrium GEM-E3_PT models are examined using a common baseline scenario to calibrate them, and the extend of their different mitigation options and its relevant to domestic policy making are assessed. Three low-carbon scenarios for Portugal until 2050 are generated, each with different GHG reduction targets. Both models suggest close mitigation options and locate the largest mitigation potential to energy supply. However, the models suggest different mitigation options for the end-use sectors: GEM-E3_PT focuses more on energy efficiency, while TIMES_PT relies on decrease carbon intensity due to a shift to electricity. Although a common baseline scenario cannot be ignored, the models’ inherent characteristics are the main factor for the different outcomes, thereby highlighting different mitigation options.

Policy relevance

The relevance of modelling tools used to support the design of domestic climate policies is assessed by evaluating the mitigation options suggested by a bottom-up and a top-down model. The different outcomes of each model are significant for climate policy design since each suggest different mitigation options like end-use energy efficiency and the promotion of low-carbon technologies. Policy makers should carefully select the modelling tool used to support their policies. The specific modelling structures of each model make them more appropriate to address certain policy questions than others. Using both modelling approaches for policy support can therefore bring added value and result in more robust climate policy design. Although the results are specific for Portugal, the insights provided by the analysis of both models can be extended to, and used in the climate policy decisions of, other countries.     

Possible climatic impacts of tropical deforestation

Large-scale conversion of tropical forests into pastures or annual crops will likely lead to changes in the local microclimate of those regions. Larger diurnal fluctuations of surface temperature and humidity deficit, increased surface runoff during rainy periods and decreased runoff during the dry season, and decreased soil moistrue are to be expected.It is likely that evapotranspiration will be reduced because of less available radiative energy at the canopy level since grass presents a higher albedo than forests, also because of the reduced availability of soil moisture at the rooting zone primarily during the dry season. Recent results from general circulation model (GCM) simulations of Amazonian deforestation seem to suggest that the equilibrium climate for a grassy vegetation in Amazonia would be one in which regional precipitation would be significantly reduced.Global climate changes probably will occur if there is a marked change in rainfall patterns in tropical forest regions as a result of deforestation. Besides that, biomass burning of tropical forests is likely adding CO₂ into the atmosphere, thus contributing to the enhanced greenhouse warming.