Regional Climate Model Scenarios Research Articles

Impact of varying the complexity of the land surface energy balance on the sensitivity of the Australian climate to increasing carbon dioxide

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 25:191-203 (2004) - doi:10.3354/cr025191 Impact of varying the complexity of the land surface energy balance on the sensitivity of the Australian climate to increasing carbon dioxide A. J. Pitman*, B. J. McAvaney Department of Physical Geography, Macquarie University, North Ryde, 2109 New South Wales, Australia *Email: apitman@penman.es.mq.edu.au ABSTRACT: This study explores whether regional climate model scenarios are sensitive to uncertainty in the representation of surface energy balance (SEB) complexity. Simulations with the Bureau of Meteorology Research Centre climate model and the CHAmeleon Surface Model (CHASM) were used to explore the sensitivity of changes in air temperature (T), evaporation (E), precipitation (P) and soil moisture (W) over Australia resulting from a doubling of atmospheric carbon dioxide (ΔCO2). The 1 × CO2 and the 2 × CO2 simulations of T, E, P and W were sensitive to the complexity of the SEB, even though the grand mean of these quantities was almost always insensitive to SEB complexity. Seasonal variations in T, E, P and W at 1 × CO2 and 2 × CO2 were sensitive in terms of the point-by-point temporal mean and temporal variance. The overall spatial and temporal variances of T and P were insensitive to SEB complexity, but E and W were sensitive during periods of drying. The simulated seasonal change in T, E, P and W was insensitive to the SEB, and uncertainty in SEB parameterisation does not limit the reliability of existing climate change scenarios for Australia. However, the temporal variance of E, P and W was sensitive to the SEB complexity during periods of drying. Use of temporal variances of these quantities in future impact assessments are therefore likely to be very limited until uncertainty in the representation of SEB in climate models is reduced. To simulate the climate over Australia at either 1 × CO2 or 2 × CO2, a reasonably complex representation of the SEB, including a temporally and spatially variable surface resistance and an explicit representation of canopy interception, is required. Finally, our results say nothing about the importance of the land surface in general, since our analysis is restricted to a consideration of the SEB alone. KEY WORDS: Surface energy balance · Land surface models · Climate changes · Land surface complexity · Australian climate · Climate modelling Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 25, No. 3. Online publication date: January 23, 2004 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2004 Inter-Research.

Hydrological Change – Climate Change Impact Simulations for Sweden

Climate change resulting from the enhanced greenhouse effect is expected to give rise to changes in hydrological systems. This hydrological change, as with the change in climate variables, will vary regionally around the globe. Impact studies at local and regional scales are needed to assess how different regions will be affected. This study focuses on assessment of hydrological impacts of climate change over a wide range of Swedish basins. Different methods of transferring the signal of climate change from climate models to hydrological models were used. Several hydrological model simulations using regional climate model scenarios from Swedish Regional Climate Modelling Programme (SWECLIM) are presented. A principal conclusion is that subregional impacts to river flow vary considerably according to whether a basin is in northern or southern Sweden. Furthermore, projected hydrological change is just as dependent on the choice of the global climate model used for regional climate model boundary conditions as the choice of anthropogenic emissions scenario.

Comparison of climate change scenarios for Sweden based on statistical and dynamical downscaling of monthly precipitation

Two dynamically and statistically downscaled precipitation scenarios for Sweden are compared with respect to changes in the mean. The dynamically downscaled scenarios are generated by a 44 km version of the Rossby Centre regional climate model (RCM). The RCM is driven by data from 2 global greenhouse gas simulations sharing a 2.6°C global warming, one made by the HadCM2 and the other by the ECHAM4 general circulation model (GCM). The statistical downscaling model driven by the same GCMs is regression-based and incorporates large-scale circulation indices of the 2 geostrophic wind components (u and v), total vorticity (ξ) and large-scale humidity at 850 hPa (q 850) as predictors. The precipitation climates of the GCMs, RCMs and statistical models from the control runs are compared with respect to their ability to reproduce the observed seasonal cycle. Great improvements in the simulation of the seasonal cycle by all the downscaling models compared to the GCMs significantly increase the credibility of the downscaling models. The precipitation changes produced by the statistical models result from changes in all predictors, but the change in ξ is the greatest contributor in southern Sweden followed by q850 and u, while changes in q850 have greater effects in the northern parts of the country. The temporal and spatial variability of precipita- tion changes are higher in the statistically downscaled scenarios than in the dynamically downscaled ones. Comparisons of the 4 scenarios show that the spread of the scenarios created by the statistical model is on average larger than that between the RCM scenarios. The relatively large average spread is mainly due to the large differences found in summer. The seasonally averaged difference of the dynamical and statistical scenarios for the ECHAM4-based downscaled scenarios is 12%, and for the HadCM2 downscaled scenarios 21%. The differences in annual precipitation change are smaller, on average 4.5% among the HadCM2-based downscaled scenarios, and 6.9% among the ECHAM4-based downscaling scenarios.

Assessment of Climate Change and Agriculture in Spain Using Climate Models

The outputs of a general circulation model (GCM) and a regional climate model (RCM) were used as inputs for crop models to predict the impact of climate change and CO2 concentration (2×CO2 scenario) on crops and their water use in Spain. Two basins representative of irrigated agriculture were chosen to compare the phenology, aboveground biomass, yield, evapotranspiration (ET), and irrigation requirements for winter wheat (Triticum aestivum L.), winter barley (Hordeum vulgare L.), and maize (Zea mays L.) under (i) a GCM‐derived climate scenario and (ii) a RCM climate output. Spatial and temporal differences in crop performance could be enhanced by the higher resolution of the RCM. Crop phenology simulated in a 1×CO2 RCM scenario was closer to the observed phenology than the GCM‐derived one. In the 1×CO2 scenario, vegetative phenophases were shorter and reflected the higher air temperatures in the RCM. In the 2×CO2 GCM and RCM scenarios, the crop duration, ET, and irrigation requirements diminished, with no significant changes in the yield and biomass between scenarios of the same model. The higher temperatures in the RCM‐derived scenarios led to higher development rates and shorter phenophases. Nevertheless, the biomass was greater than in the GCM‐derived scenarios, especially in C3 crops. Higher temperatures in the southern areas led to a lack of vernalization in the future climate scenario, which was only detected by the RCM‐DSSAT (Decision Support System for Agrotechnology Transfer). The use of direct output of the GCM is questionable given the orography, land cover, and size of the Iberian Peninsula, and it makes the use of high‐resolution models necessary.

Adaptation: Sensitivity to Natural Variability, Agent Assumptions and Dynamic Climate Changes

The role of adaptation in impact assessment and integrated assessment of climate policy is briefly reviewed. Agriculture in the US is taken as exemplary of this issue. Historic studies in which no adaptation is assumed (so-called "dumb farmer") versus farmer-agents blessed with perfect foresight (so-called "clairvoyant farmer") are contrasted, and considered limiting cases as compared to "realistic farmers." What kinds of decision rules such realistic farmer-agents would adopt to deal with climate change involves a range of issues. These include degrees of belief the climate is actually changing, knowledge about how it will change, foresight on how technology is changing, estimation of what will happen in competitive granaries and assumptions about what governmental policies will be in various regions and over time. Clearly, a transparent specification of such agent-based decision rules is essential to model adaptation explicitly in any impact assessment. Moreover, open recognition of the limited set of assumptions contained in any one study of adaptation demands that authors clearly note that each individual study can represent only a fraction of plausible outcomes. A set of calculations using the Erosion Productivity Impact Calculator (EPIC) crop model is offered here as an example of explicit decision rules on adaptive behavior on climate impacts. The model is driven by a 2xCO2 regional climate model scenario (from which a "mock" transient scenario was devised) to calculate yield changes for farmer-agents that practice no adaptation, perfect adaptation and 20-year-lagged adaptation, the latter designed to mimic the masking effects of natural variability on farmers' capacity to see how climate is changing. The results reinforce the expectation that the likely effects of natural variability, which would mask a farmer's capacity to detect climate change, is to place the calculated impacts of climate changes in two regions of the US in between that of perfect and no adaptation. Finally, the use of so-called "hedonic" methods (in which land prices in different regions with different current average climates are used to derive implicitly farmers' adaptive responses to hypothesized future climate changes) is briefly reviewed. It is noted that this procedure in which space and time are substituted, amounts to "ergodic economics." Such cross-sectional analyses are static, and thus neglect the dynamics of both climate and societal evolution. Furthermore, such static methods usually consider only a single measure of change (local mean annual temperature), rather than higher moments like climatic variability, diurnal temperature range, etc. These implicit assumptions in ergodic economics make use of such cross-sectional studies limited for applications to integrated assessments of the actual dynamics of adaptive capacity. While all such methods are appropriate for sensitivity analyses and help to define a plausible range of outcomes, none is by itself likely to define the range of plausible adaptive capacities that might emerge in response to climate change scenarios.