Climate change scenarios for surface temperature in Emilia-Romagna (Italy) obtained using statistical downscaling models

Summary Possible changes of mean climate and the frequency of extreme temperature events in Emilia-Romagna, over the period 2070–2100 compared to 1960–1990, are assessed. A statistical downscaling technique, applied to HadAM3P experiments (control, A2 and B2 scenarios) performed at the Hadley Centre, is used to achieve this objective. The method applied consists of a multivariate regression based on Canonical Correlation Analysis (CCA), using as possible predictors mean sea level pressure (MSLP), geopotential height at 500 hPa (Z500) and temperature at 850 hPa (T850), and as predictands the seasonal mean values of minimum and maximum surface temperature (T_min and T_max), 90^th percentile of maximum temperature (T_max90), 10^th percentile of minimum temperature (T_min10), number of frost days (T_nfd) and heat wave duration (HWD) at the station level. First, the statistical model is optimised and calibrated using NCEP/NCAR reanalysis to evaluate the large-scale predictors. The observational data at 32 stations uniformly distributed over Emilia-Romagna are used to compute the local predictands. The results of the optimisation procedure reveal that T850 is the best predictor in most cases, and in combination with MSLP, is an optimum predictor for winter T_max90 and autumn T_min10. Finally, MSLP is the best predictor for spring T_min while Z500 is the best predictor for spring T_max90 and heat wave duration index, except during autumn. The ability of HadAM3P to simulate the present day spatial and temporal variability of the chosen predictors is tested using the control experiments. Finally, the downscaling model is applied to all model output experiments to obtain simulated present day and A2 and B2 scenario results at the local scale. Results show that significant increases can be expected to occur under scenario conditions in both maximum and minimum temperature, associated with a decrease in the number of frost days and with an increase in the heat wave duration index. The magnitude of the change is more significant for the A2 scenario than for the B2 scenario.     

Daily relative humidity projections in an Indian river basin for IPCC SRES scenarios

A two-stage methodology is developed to obtain future projections of daily relative humidity in a river basin for climate change scenarios. In the first stage, Support Vector Machine (SVM) models are developed to downscale nine sets of predictor variables (large-scale atmospheric variables) for Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (SRES) (A1B, A2, B1, and COMMIT) to R _H in a river basin at monthly scale. Uncertainty in the future projections of R _H is studied for combinations of SRES scenarios, and predictors selected. Subsequently, in the second stage, the monthly sequences of R _H are disaggregated to daily scale using k-nearest neighbor method. The effectiveness of the developed methodology is demonstrated through application to the catchment of Malaprabha reservoir in India. For downscaling, the probable predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis data set for the period 1978–2000 and (2) simulations of the third-generation Canadian Coupled Global Climate Model for the period 1978–2100. The performance of the downscaling and disaggregation models is evaluated by split sample validation. Results show that among the SVM models, the model developed using predictors pertaining to only land location performed better. The R _H is projected to increase in the future for A1B and A2 scenarios, while no trend is discerned for B1 and COMMIT.     

CGCM3 predictors used for daily temperature and precipitation downscaling in Southern Québec, Canada

This study provides some guidance on the choice of predictor variables from both reanalysis products and the third version of the Canadian Coupled Global Climate Model (CGCM3) outputs for regression-based statistical downscaling models (SDMs) for climate change application in southern Québec (Canada). Twenty CGCM3 grid points and four surface observation sites in the study area were employed. Twenty-five deseasonalized predictors and four deseasonalized predictands (daily maximum and minimum temperatures, precipitation occurrence and wet day precipitation amount) were used to investigate correlation coefficients among predictors and to evaluate their predictive ability when used in a multiple linear regression (MLR) downscaling model. The basic statistical characteristics of vorticity at 1,000-, 850- and 500-hPa levels, U-component of velocity at 1,000-hPa level, temperature at 2?m (T ₂) and wind direction at 1,000- and 500-hPa level of CGCM3 showed a larger difference with those of the NCEP reanalysis data. Therefore, those seven variables require high caution to be included as predictors in statistical downscaling models. Specific humidity at 1,000-, 850- and 500-hPa levels, geopotential height at 850- and 500-hPa levels and T ₂ were the most sensitive predictors for future climate conditions (i.e. A1B and A2 emission scenarios). Specific humidity and geopotential height at different levels and T ₂ were important explainable predictors for the daily temperatures. Mean sea level pressure, specific humidity, U and V components and divergence showed potential as predictors for daily precipitation. Spatial explained variance of MLRs between predictors of every different CGCM3 grid points and the four predictands showed large values at the CGCM3 grid points located near the observation sites, whereas relatively small values were shown at the CGCM3 grid points located more than 400?km from the sites. The explained variance of the downscaled predictands by predictors of three or four CGCM3 grid points located near the observation site produced 2–5% larger R-squares than those by predictors of the nearest grid point. The results illustrated that the use of predictors from more than one AOGCM grid points located near the observation site can increase the skill of the MLR downscaling models.     

Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series

This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000–2009, 2046–2065 and 2081–2100, using the period of 1962–1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000–2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales.     

Analysis of the change in temperature trends in Subansiri River basin for RCP scenarios using CMIP5 datasets

This study focuses on changes in the maximum and minimum temperature over the Subansiri River basin for different climate change scenarios. For the study, dataset from Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5) (i.e., coupled model intercomparison project phase five (CMIP5) dataset with representative concentration pathway (RCP) scenarios) were utilized. Long-term (2011–2100) maximum temperature (T _max) and minimum temperature (T_min) time series were generated using the statistical downscaling technique for low emission scenario (RCP2.6), moderate emission scenario (RCP6.0), and extreme emission scenario (RCP8.5). Trends and change of magnitude in T _max, T _min, and diurnal temperature range (DTR) were analyzed for different interdecadal time scales (2011–2100, 2011–2040, 2041–2070, 2070–2100) using Mann-Kendall non-parametric test and Sen’s slope estimator, respectively. The temperature data series for the observed duration (1981–2000) has been found to show increasing trends in T _max and T _min at both annual and monthly scale. Trend analysis of downscaled temperature for the period 2011–2100 shows increase in annual maximum temperature and annual minimum temperature for all the selected RCP scenarios; however, on the monthly scale, T _max and T _min have been seen to have decreasing trends in some months.

     

Seasonal reversal at Miryang Eoreumgol (Ice Valley), Korea: observation and monitoring

Climate change information required for impact studies is of a much finer scale than that provided by Global circulation models (GCMs). This paper presents an application of partial least squares (PLS) regression for downscaling GCMs output. Statistical downscaling models were developed using PLS regression for simultaneous downscaling of mean monthly maximum and minimum temperatures (T _max and T _min) as well as pan evaporation to lake-basin scale in an arid region in India. The data used for evaluation were extracted from the NCEP/NCAR reanalysis dataset for the period 1948?C2000 and the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1, and COMMIT for the period 2001?C2100. A simple multiplicative shift was used for correcting predictand values. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response. The analysis of downscaling models reveals that (1) the correlation coefficient for downscaled versus observed mean maximum temperature, mean minimum temperature, and pan evaporation was 0.94, 0.96, and 0.89, respectively; (2) an increasing trend is observed for T _max and T _min for A1B, A2, and B1 scenarios, whereas no trend is discerned with the COMMIT scenario; and (3) there was no trend observed in pan evaporation. In COMMIT scenario, atmospheric CO₂ concentrations are held at year 2000 levels. Furthermore, a comparison with neural network technique shows the efficiency of PLS regression method.     

Future surface mass balance of the Antarctic ice sheet and its influence on sea level change, simulated by a regional atmospheric climate model

A regional atmospheric climate model with multi-layer snow module (RACMO2) is forced at the lateral boundaries by global climate model (GCM) data to assess the future climate and surface mass balance (SMB) of the Antarctic ice sheet (AIS). Two different GCMs (ECHAM5 until 2100 and HadCM3 until 2200) and two different emission scenarios (A1B and E1) are used as forcing to capture a realistic range in future climate states. Simulated ice sheet averaged 2 m air temperature (T_2m) increases (1.8–3.0 K in 2100 and 2.4–5.3 K in 2200), simultaneously and with the same magnitude as GCM simulated T_2m. The SMB and its components increase in magnitude, as they are directly influenced by the temperature increase. Changes in atmospheric circulation around Antarctica play a minor role in future SMB changes. During the next two centuries, the projected increase in liquid water flux from rainfall and snowmelt, together 60–200 Gt year^?1, will mostly refreeze in the snow pack, so runoff remains small (10–40 Gt year^?1). Sublimation increases by 25–50 %, but remains an order of magnitude smaller than snowfall. The increase in snowfall mainly determines future changes in SMB on the AIS: 6–16 % in 2100 and 8–25 % in 2200. Without any ice dynamical response, this would result in an eustatic sea level drop of 20–43 mm in 2100 and 73–163 mm in 2200, compared to the twentieth century. Averaged over the AIS, a strong relation between $\Updelta$ SMB and $\Updelta\hbox{T}_{2{\rm m}}$ of 98 ± 5 Gt w.e. year^?1 K^?1 is found.     

Constructing Site-Specific Climate Change Scenarios on a Monthly Scale Using Statistical Downscaling

Summary Monthly mean temperature and monthly precipitation totals in two small catchments in the Czech Republic are estimated from large-scale 500 hPa height and 1000/500 hPa thickness fields using statistical downscaling. The method used is multiple linear regression. Whereas precipitation can be determined from large-scale fields with some confidence in only a few months of the year, temperature can be determined successfully. Principal components calculated separately from the height and thickness anomalies are identified as the best predictor set. The method is most accurate if the regression is performed using seasons based on three months. The test on an independent sample, consisting of warm seasons, confirms that the method successfully reproduces the difference in mean temperature between two climatic states, which indicates that this downscaling method is applicable for constructing scenarios of a future climate change. The ECHAM3 GCM is used for scenario construction. The GCM is shown to simulate surface temperature and precipitation with low accuracy, whereas the large-scale atmospheric fields are reproduced well; this justifies the downscaling approach. The observed regression equations are applied to 2xCO₂ GCM output so that the model’s bias is eleminated. This procedure is then discussed and finally, temperature scenarios for the 2xCO₂ climate are constructed for the two catchments. Received December 3, 1998 Revised December 4, 1999     

Potential climate change impacts on the probability of wind damage in a south Swedish forest

We estimated how the possible changes in wind climate and state of the forest due to climate change may affect the probability of exceeding critical wind speeds expected to cause wind damage within a forest management unit located in Southern Sweden. The topography of the management unit was relatively gentle and the forests were dominated by Norway spruce (Picea abies (L.) Karst.). We incorporated a model relating the site index (SI) to the site productivity into the forest projection model FTM. Using estimated changes in the net primary production (NPP) due to climate change and assuming a relative change in NPP equal to a relative change in the site productivity, we simulated possible future states of the forest under gradual adjustment of SI in response to climate change. We estimated changes in NPP by combining the boreal-adapted BIOMASS model with four regional climate change scenarios calculated using the RCAO model for the period 2071–2100 and two control period scenarios for the period 1961–1990. The modified WINDA model was used to calculate the probability of wind damage for individual forest stands in simulated future states of the forest. The climate change scenarios used represent non-extreme projections on a 100-year time scale in terms of global mean warming. A 15–40% increase in NPP was estimated to result from climate change until the period 2071–2100. Increasing sensitivity of the forest to wind was indicated when the management rules of today were applied. A greater proportion of the calculated change in probability of wind damage was due to changes in wind climate than to changes in the sensitivity of the forest to wind. While regional climate scenarios based on the HadAM3H general circulation model (GCM) indicated no change (SRES A2 emission scenario) or a slightly reduced (SRES B2 emission scenario) probability of wind damage, scenarios based on the ECHAM4/OPYC3 GCM indicated increased probability of wind damage. The assessment should, however, be reviewed as the simulation of forest growth under climate change as well as climate change scenarios are refined.     

Statistical downscaling of daily climate variables for climate change impact assessment over New South Wales, Australia

This paper outlines a new statistical downscaling method based on a stochastic weather generator. The monthly climate projections from global climate models (GCMs) are first downscaled to specific sites using an inverse distance-weighted interpolation method. A bias correction procedure is then applied to the monthly GCM values of each site. Daily climate projections for the site are generated by using a stochastic weather generator, WGEN. For downscaling WGEN parameters, historical climate data from 1889 to 2008 are sorted, in an ascending order, into 6 climate groups. The WGEN parameters are downscaled based on the linear and non-linear relationships derived from the 6 groups of historical climates and future GCM projections. The overall averaged confidence intervals for these significant linear relationships between parameters and climate variables are 0.08 and 0.11 (the range of these parameters are up to a value of 1.0) at the observed mean and maximum values of climate variables, revealing a high confidence in extrapolating parameters for downscaling future climate. An evaluation procedure is set up to ensure that the downscaled daily sequences are consistent with monthly GCM output in terms of monthly means or totals. The performance of this model is evaluated through the comparison between the distributions of measured and downscaled climate data. Kruskall-Wallis rank (K-W) and Siegel-Tukey rank sum dispersion (S-T) tests are used. The results show that the method can reproduce the climate statistics at annual, monthly and daily time scales for both training and validation periods. The method is applied to 1062 sites across New South Wales (NSW) for 9 GCMs and three IPCC SRES emission scenarios, B1, A1B and A2, for the period of 1900–2099. Projected climate changes by 7 GCMs are also analyzed for the A2 emission scenario based on the downscaling results.     

Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania

Summary Regional climate model and statistical downscaling procedures are used to generate winter precipitation changes over Romania for the period 2071–2100 (compared to 1961–1990), under the IPCC A2 and B2 emission scenarios. For this purpose, the ICTP regional climate model RegCM is nested within the Hadley Centre global atmospheric model HadAM3H. The statistical downscaling method is based on the use of canonical correlation analysis (CCA) to construct climate change scenarios for winter precipitation over Romania from two predictors, sea level pressure and specific humidity (either used individually or together). A technique to select the most skillful model separately for each station is proposed to optimise the statistical downscaling signal. Climate fields from the A2 and B2 scenario simulations with the HadAM3H and RegCM models are used as input to the statistical downscaling model. First, the capability of the climate models to reproduce the observed link between winter precipitation over Romania and atmospheric circulation at the European scale is analysed, showing that the RegCM is more accurate than HadAM3H in the simulation of Romanian precipitation variability and its connection with large-scale circulations. Both models overestimate winter precipitation in the eastern regions of Romania due to an overestimation of the intensity and frequency of cyclonic systems over Europe. Climate changes derived directly from the RegCM and HadAM3H show an increase of precipitation during the 2071–2100 period compared to 1961–1990, especially over northwest and northeast Romania. Similar climate change patterns are obtained through the statistical downscaling method when the technique of optimum model selected separately for each station is used. This adds confidence to the simulated climate change signal over this region. The uncertainty of results is higher for the eastern and southeastern regions of Romania due to the lower HadAM3H and RegCM performance in simulating winter precipitation variability there as well as the reduced skill of the statistical downscaling model.     

Scaling Issues in Forest Succession Modelling

This paper reviews scaling issues in forest succession modelling, focusing on forest gap models. Two modes of scaling are distinguished: (1) implicit scaling, i.e. taking scale-dependent features into account while developing model equations, and (2) explicit scaling, i.e. using procedures that typically involve numerical simulation to scale up the response of a local model in space and/or time. Special attention is paid to spatial upscaling methods, and downscaling is covered with respect to deriving scenarios of climatic change to drive gap models in impact assessments. When examining the equations used to represent ecological processes in forest gap models, it becomes evident that implicit scaling is relevant, but has not always been fully taken into consideration. A categorization from the literature is used to distinguish four methods for explicit upscaling of ecological models in space: (1) Lumping, (2) Direct extrapolation, (3) Extrapolation by expected value, and (4) Explicit integration. Examples from gap model studies are used to elaborate the potential and limitations of these methods, showing that upscaling to areas as large as 3000 km² is possible, given that there are no significant disturbances such as fires or insect outbreaks at the landscape scale. Regarding temporal upscaling, we find that it is important to consider migrational lags, i.e. limited availability of propagules, if one wants to assess the transient behaviour of forests in a changing climate, specifically with respect to carbon storage and the associated feedbacks to the atmospheric CO₂ content. Regarding downscaling, the ecological effects of different climate scenarios for the year 2100 were compared at a range of sites in central Europe. The derivation of the scenarios is based on (1) imposing GCM grid-cell average changes of temperature and precipitation on the local weather records; (2) a qualitative downscaling technique applied by the IPCC for central and southern Europe; and (3) statistical downscaling relating large-scale circulation patterns to local weather records. Widely different forest compositions may be obtained depending on the local climate scenario, suggesting that the downscaling issue is quite important for assessments of the ecological impacts of climatic change on forests.     

Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec,Canada

Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.     

Statistical downscaling of daily precipitation over Sweden using GCM output

A classification of Swedish weather patterns (SWP) was developed by applying a multi-objective fuzzy-rule-based classification method (MOFRBC) to large-scale-circulation predictors in the context of statistical downscaling of daily precipitation at the station level. The predictor data was mean sea level pressure (MSLP) and geopotential heights at 850 (H850) and 700 hPa (H700) from the NCEP/NCAR reanalysis and from the HadAM3 GCM. The MOFRBC was used to evaluate effects of two future climate scenarios (A2 and B2) on precipitation patterns on two regions in south-central and northern Sweden. The precipitation series were generated with a stochastic, autoregressive model conditioned on SWP. H850 was found to be the optimum predictor for SWP, and SWP could be used instead of local classifications with little information lost. The results in the climate projection indicated an increase in maximum 5-day precipitation and precipitation amount on a wet day for the scenarios A2 and B2 for the period 2070–2100 compared to 1961–1990. The relative increase was largest in the northern region and could be attributed to an increase in the specific humidity rather than to changes in the circulation patterns.     

Assessment of the impact of climate change on the olive flowering in Calabria (southern Italy)

In phenological studies, plant development and its relationship with meteorological conditions are considered in order to investigate the influence of climatic changes on the characteristics of many crop species. In this work, the impact of climate change on the flowering of the olive tree (Olea europaea L.) in Calabria, southern Italy, has been studied. Olive is one of the most important plant species in the Mediterranean area and, at the same time, Calabria is one of the most representative regions of this area, both geographically and climatically. The work is divided into two main research activities. First, the behaviour of olive tree in Calabria and the influence of temperature on phenological phases of this crop are investigated. An aerobiological method is used to determine the olive flowering dates through the analysis of pollen data collected in three experimental fields for an 11-year study period (1999–2009). Second, the study of climate change in Calabria at high spatial and temporal resolution is performed. A dynamical downscaling procedure is applied for the regionalization of large-scale climate analysis derived from general circulation models for two representative climatic periods (1981–2000 and 2081–2100); the A2 IPCC scenario is used for future climate projections. The final part of this work is the integration of the results of the two research activities to predict the olive flowering variation for the future climatic conditions. In agreement with our previous works, we found a significant correlation between the phenological phases and temperature. For the twenty-first century, an advance of pollen season in Calabria of about 9?days, on average, is expected for each degree of temperature rise. From phenological model results, on the basis of future climate predictions over Calabria, an anticipation of maximum olive flowering between 10 and 34?days is expected, depending on the area. The results of this work are useful for adaptation and mitigation strategies, and for making concrete assessments about biological and environmental changes.     

An analogue model to derive additional climate change scenarios from existing GCM simulations

 Changes in land surface driving variables, predicted by GCM transient climate change experiments, are confirmed to exhibit linearity in the global mean land temperature anomaly, ΔT _l . The associated constants of proportionality retain spatial and seasonal characteristics of the GCM output, whilst ΔT _l is related to radiative forcing anomalies. The resultant analogue model is shown to be robust between GCM runs and as such provides a computationally efficient technique of extending existing GCM experiments to a large range of climate change scenarios. As an example impacts study, the analogue model is used to drive a terrestrial ecosystem model, and predicted changes in terrestrial carbon are found to be similar to those when using GCM anomalies directly. Received: 4 January 1999 / Accepted: 11 December 1999     

Temporal downscaling: a comparison between artificial neural network and autocorrelation techniques over the Amazon Basin in present and future climate change scenarios

Several studies have been devoted to dynamic and statistical downscaling for both climate variability and climate change. This paper introduces an application of temporal neural networks for downscaling global climate model output and autocorrelation functions. This method is proposed for downscaling daily precipitation time series for a region in the Amazon Basin. The downscaling models were developed and validated using IPCC AR4 model output and observed daily precipitation. In this paper, five AOGCMs for the twentieth century (20C3M; 1970–1999) and three SRES scenarios (A2, A1B, and B1) were used. The performance in downscaling of the temporal neural network was compared to that of an autocorrelation statistical downscaling model with emphasis on its ability to reproduce the observed climate variability and tendency for the period 1970–1999. The model test results indicate that the neural network model significantly outperforms the statistical models for the downscaling of daily precipitation variability.     

A comparison of explicit and implicit spatial downscaling of GCM output for soil erosion and crop production assessments

Spatial downscaling of climate change scenarios can be a significant source of uncertainty in simulating climatic impacts on soil erosion, hydrology, and crop production. The objective of this study is to compare responses of simulated soil erosion, surface hydrology, and wheat and maize yields to two (implicit and explicit) spatial downscaling methods used to downscale the A2a, B2a, and GGa1 climate change scenarios projected by the Hadley Centre’s global climate model (HadCM3). The explicit method, in contrast to the implicit method, explicitly considers spatial differences of climate scenarios and variability during downscaling. Monthly projections of precipitation and temperature during 1950–2039 were used in the implicit and explicit spatial downscaling. A stochastic weather generator (CLIGEN) was then used to disaggregate monthly values to daily weather series following the spatial downscaling. The Water Erosion Prediction Project (WEPP) model was run for a wheat–wheat–maize rotation under conventional tillage at the 8.7 and 17.6% slopes in southern Loess Plateau of China. Both explicit and implicit methods projected general increases in annual precipitation and temperature during 2010–2039 at the Changwu station. However, relative climate changes downscaled by the explicit method, as compared to the implicit method, appeared more dynamic or variable. Consequently, the responses to climate change, simulated with the explicit method, seemed more dynamic and sensitive. For a 1% increase in precipitation, percent increases in average annual runoff (soil loss) were 3–6 (4–10) times greater with the explicit method than those with the implicit method. Differences in grain yield were also found between the two methods. These contrasting results between the two methods indicate that spatial downscaling of climate change scenarios can be a significant source of uncertainty, and further underscore the importance of proper spatial treatments of climate change scenarios, and especially climate variability, prior to impact simulation. The implicit method, which applies aggregated climate changes at the GCM grid scale directly to a target station, is more appropriate for simulating a first-order regional response of nature resources to climate change. But for the site-specific impact assessments, especially for entities that are heavily influenced by local conditions such as soil loss and crop yield, the explicit method must be used.     

Addressing sources of uncertainty in runoff projections for a data scarce catchment in the Ecuadorian Andes

Future climate projections from general circulation models (GCMs) predict an acceleration of the global hydrological cycle throughout the 21st century in response to human-induced rise in temperatures. However, projections of GCMs are too coarse in resolution to be used in local studies of climate change impacts. To cope with this problem, downscaling methods have been developed that transform climate projections into high resolution datasets to drive impact models such as rainfall-runoff models. Generally, the range of changes simulated by different GCMs is considered to be the major source of variability in the results of such studies. However, the cascade of uncertainty in runoff projections is further elongated by differences between impact models, especially where robust calibration is hampered by the scarcity of data. Here, we address the relative importance of these different sources of uncertainty in a poorly monitored headwater catchment of the Ecuadorian Andes. Therefore, we force 7 hydrological models with downscaled outputs of 8 GCMs driven by the A1B and A2 emission scenarios over the 21st century. Results indicate a likely increase in annual runoff by 2100 with a large variability between the different combinations of a climate model with a hydrological model. Differences between GCM projections introduce a gradually increasing relative uncertainty throughout the 21st century. Meanwhile, structural differences between applied hydrological models still contribute to a third of the total uncertainty in late 21st century runoff projections and differences between the two emission scenarios are marginal.     

Use of general circulation model output in the creation of climate change scenarios for impact analysis

Many scientific studies warn of a rapid global climate change during the next century. These changes are understood with much less certainty on a regional scale than on a global scale, but effects on ecosystems and society will occur at local and regional scales. Consequently, in order to study the true impacts of climate change, regional scenarios of future climate are needed. One of the most important sources of information for creating scenarios is the output from general circulation models (GCMs) of the climate system. However, current state-of-the-art GCMs are unable to simulate accurately even the current seasonal cycle of climate on a regional basis. Thus the simple technique of adding the difference between 2 × CO₂ and 1 × CO₂ GCM simulations to current climatic time series cannot produce scenarios with appropriate spatial and temporal details without corrections for model deficiencies. In this study a technique is developed to allow the information from GCM simulations to be used, while accommodating for the deficiencies. GCM output is combined with knowledge of the regional climate to produce scenarios of the equilibrium climate response to a doubling of the atmospheric CO₂ concentration for three case study regions, China, Sub-Saharan Africa and Venezuela, for use in biological effects models. By combining the general climate change calculated with several GCMs with the observed patterns of interannual climate variability, reasonable scenarios of temperature and precipitation variations can be created. Generalizations of this procedure to other regions of the world are discussed.