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.     

USE OF A STOCHASTIC WEATHER GENERATOR IN THE DEVELOPMENT OF CLIMATE CHANGE SCENARIOS

Climate change scenarios with a high spatial and temporal resolution are required in the evaluation of the effects of climate change on agricultural potential and agricultural risk. Such scenarios should reproduce changes in mean weather characteristics as well as incorporate the changes in climate variability indicated by the global climate model (GCM) used. Recent work on the sensitivity of crop models and climatic extremes has clearly demonstrated that changes in variability can have more profound effects on crop yield and on the probability of extreme weather events than simple changes in the mean values. The construction of climate change scenarios based on spatial regression downscaling and on the use of a local stochastic weather generator is described. Regression downscaling translated the coarse resolution GCM grid-box predictions of climate change to site-specific values. These values were then used to perturb the parameters of the stochastic weather generator in order to simulate site-specific daily weather data. This approach permits the incorporation of changes in the mean and variability of climate in a consistent and computationally inexpensive way. The stochastic weather generator used in this study, LARS-WG, has been validated across Europe and has been shown to perform well in the simulation of different weather statistics, including those climatic extremes relevant to agriculture. The importance of downscaling and the incorporation of climate variability are demonstrated at two European sites where climate change scenarios were constructed using the UK Met. Office high resolution GCM equilibrium and transient experiments.     

Simulating forest dynamics in a complex topography using gridded climatic data

The forest model ForClim was used to evaluate the applicability of gap models in complex topography when the climatic input data is provided by a global database of 0.5° resolution. The analysis was based on 12 grid cells along an altitudinal gradient in the European Alps. Forest dynamics were studied both under current climate as well as under four prescribed 2 × CO₂ scenarios of climatic change obtained from General Circulation Models, which allowed to assess the sensitivity of mountainous forests to climatic change.Under current climate, ForClim produces plausible patterns of species composition in space and time, although the results for single grid cells sometimes are not representative of reality due to the limited precision of the climatic input data.Under the scenarios of climatic change, three responses of the vegetation are observed, i.e., afforestation, gradual changes of the species composition, and dieback of today's forest. In some cases widely differing species compositions are obtained depending on the climate scenario used, suggesting that mountainous forests are quite sensitive to climatic change. Some of the new forests have analogs on the modern landscape, but in other cases non-analog communities are formed, pointing at the importance of the individualistic response of species to climate.The applicability of gap models on a regular grid in a complex topography is discussed. It is concluded that for their application on a continental scale, it would be desirable to replace the species in the models by plant functional types. It is suggested that simulation studies like the present one must not be interpreted as predictions of the future fate of forests, but as means to assess their sensitivity to climatic change.     

Integrating climatic change and forests: Economic and ecologic assessments

Effective policies for dealing with anticipated climatic changes must reflect the two-way interactions between climate, forests and society. Considerable analysis has focused on one aspect of forests - timber production - at a local and regional scale, but no fully integrated global studies have been conducted. The appropriate ecological and economic models appear to be available to do so. Nontimber aspects of forests dominate the social values provided by many forests, especially remote or unmanaged lands where the impacts of climatic change are apt to be most significant. Policy questions related to these issues and lands are much less well understood. Policy options related to afforestation are well studied, but other ways the forest sector can help ameliorate climatic change merit more extensive analysis. Promising possibilities include carbon taxes to influence the management of extant forests, and materials policies to lengthen the life of wood products or to encourage the substitution of CO₂-fixing wood products for ones manufactured from less benign materials.     

近二十年来暴雨和强对流可预报性研究进展

大气可预报性研究是开展天气、气候预测的基础科学问题。全球变暖背景下,近年暴雨和强对流等中小尺度灾害性天气频发,如何深入认识其可预报性问题成为了天气领域研究热点,也是制约数值天气预报模式能力提升的重要因素。本文在简要回顾国内外大气可预报性研究历程的基础上,重点对近二十年（1999～2018）国际上关于暴雨和强对流可预报性方面的最新研究进展进行了系统的综述和归纳。主要包括:中小尺度可预报性研究的主要方法和评估手段及其与传统大尺度天气可预报性研究的差异,初始误差增长机制的几种主要观点及其争论（误差升尺度、误差降尺度、升降尺度并存）,数值模式误差和对流环境误差对实际预报性的影响,以及最近的中尺度可预报性科学观测试验进展等。最后,对暴雨、强对流可预报性研究存在的问题、未来发展方向进行了简要的讨论和展望。     

Comparing the Performance of Forest gap Models in North America

Forest gap models have a long history in the study of forest dynamics, including predicting long-term succession patterns and assessing the potential impacts of climate change and air pollution on forest structure and composition. In most applications, existing models are adapted for the specific question at hand and little effort is devoted to evaluating alternative formulations for key processes, although this has the potential to significantly influence model behavior. In the present study, we explore the implications of alternative formulations for selected ecological processes via the comparison of several gap models. Baseline predictions of forest biomass, composition and size structure generated by several gap models are compared to each other and to measured data at boreal and temperate sites in North America. The models ForClim and LINKAGES v2.0 were compared based on simulations of a temperate forest site in Tennessee, whereas FORSKA-2V, BOREALIS and ForClim were compared at four boreal forest sites in central and eastern Canada. Results for present-day conditions were evaluated on their success in predicting forest cover, species composition, total biomass and stand density, and allocation of biomass among species. In addition, the sensitivity of each model to climatic changes was investigated using a suite of six climate change scenarios involving temperature and precipitation. In the temperate forest simulations, both ForClim and LINKAGES v2.0 predicted mixed mesophytic forests dominated by oak species, which is expected for this region of Tennessee. The models differed in their predictions of species composition as well as with respect to the simulated rates of succession. Simulated forest dynamics under the changed climates were qualitatively similar between the two models, although aboveground biomass and species composition in ForClim was more sensitive to drought than in LINKAGES v2.0. Under a warmer climate, the modeled effects of temperature on tree growth in LINKAGES v2.0 led to the unrealistic loss of several key species. In the boreal forest simulations, ForClim predicted significant forest growth at only the most mesic site, and failed to predict a realistic species composition. In contrast, FORSKA-2V and BOREALIS were successful in simulating forest cover, general species composition, and biomass at most sites. In the climate change scenarios, ForClim was highly sensitive, whereas the other two models exhibited sensitivity only at the drier central Canadian sites. Although the studied sites differ strongly with respect to both the climatic regime and the set of dominating species, a unifying feature emerged from these simulation exercises. The major differences in model behavior were brought about by differences in the internal representations of the seasonal water balance, and they point to an important limitation in some gap model formulations for assessing climate change impacts.     

User comments on climate stories: impacts of anecdotal vs. scientific evidence

Both statistical and dynamical downscaling methods are well established techniques to bridge the gap between the coarse information produced by global circulation models and the regional-to-local scales required by the climate change Impacts, Adaptation, and Vulnerability (IAV) communities. A number of studies have analyzed the relative merits of each technique by inter-comparing their performance in reproducing the observed climate, as given by a number of climatic indices (e.g. mean values, percentiles, spells). However, in this paper we stress that fair comparisons should be based on indices that are not affected by the calibration towards the observed climate used for some of the methods. We focus on precipitation (over continental Spain) and consider the output of eight Regional Climate Models (RCMs) from the EURO-CORDEX initiative at 0.44^° resolution and five Statistical Downscaling Methods (SDMs) —analog resampling, weather typing and generalized linear models— trained using the Spain044 observational gridded dataset on exactly the same RCM grid. The performance of these models is inter-compared in terms of several standard indices —mean precipitation, 90th percentile on wet days, maximum precipitation amount and maximum number of consecutive dry days— taking into account the parameters involved in the SDM training phase. It is shown, that not only the directly affected indices should be carefully analyzed, but also those indirectly influenced (e.g. percentile-based indices for precipitation) which are more difficult to identify. We also analyze how simple transformations (e.g. linear scaling) could be applied to the outputs of the uncalibrated methods in order to put SDMs and RCMs on equal footing, and thus perform a fairer comparison.     

Development of climate change projections for small watersheds using multi-model ensemble simulation and stochastic weather generation

Regional climate models (RCMs) have been increasingly used for climate change studies at the watershed scale. However, their performance is strongly dependent upon their driving conditions, internal parameterizations and domain configurations. Also, the spatial resolution of RCMs often exceeds the scales of small watersheds. This study developed a two-step downscaling method to generate climate change projections for small watersheds through combining a weighted multi-RCM ensemble and a stochastic weather generator. The ensemble was built on a set of five model performance metrics and generated regional patterns of climate change as monthly shift terms. The stochastic weather generator then incorporated these shift terms into observed climate normals and produced synthetic future weather series at the watershed scale. This method was applied to the Assiniboia area in southern Saskatchewan, Canada. The ensemble led to reduced biases in temperature and precipitation projections through properly emphasizing models with good performance. Projection of precipitation occurrence was particularly improved through introducing a weight-based probability threshold. The ensemble-derived climate change scenario was well reproduced as local daily weather series by the stochastic weather generator. The proposed combination of dynamical downscaling and statistical downscaling can improve the reliability and resolution of future climate projection for small prairie watersheds. It is also an efficient solution to produce alternative series of daily weather conditions that are important inputs for examining watershed responses to climate change and associated uncertainties.     

Tree Species Composition in European Pristine Forests: Comparison of Stand Data to Model Predictions

The degree of general applicability across Europe currently achieved with several forest succession models is assessed, data needs and steps for further model development are identified and the role physiology based models can play in this process is evaluated. To this end, six forest succession models (DISCFORM, ForClim, FORSKA-M, GUESS, PICUS v1.2, SIERRA) are applied to simulate stand structure and species composition at 5 European pristine forest sites in different climatic regions. The models are initialized with site-specific soil information and driven with climate data from nearby weather stations. Predicted species composition and stand structure are compared to inventory data. Similarity and dissimilarity in the model results under current climatic conditions as well as the predicted responses to six climate change scenarios are discussed. All models produce good results in the prediction of the right tree functional types. In about half the cases, the dominating species are predicted correctly under the current climate. Where deviations occur, they often represent a shift of the species spectrum towards more drought tolerant species. Results for climate change scenarios indicate temperature driven changes in the alpine elevational vegetation belts at humid sites and a high sensitivity of forest composition and biomass of boreal and temperate deciduous forests to changes in precipitation as mediated by summer drought. Restricted generality of the models is found insofar as models originally developed for alpine conditions clearly perform better at alpine sites than at boreal sites, and vice versa. We conclude that both the models and the input data need to be improved before the models can be used for a robust evaluation of forest dynamics under climate change scenarios across Europe. Recommendations for model improvements, further model testing and the use of physiology based succession models are made.     

Aboveground Growth and Competition in Forest Gap Models: An Analysis for Studies of Climatic Change

Gap models have been used extensively in ecological studies of forest structure and succession, and they should be useful tools for studying potential responses of forests to climatic change. There is a wide variety of gap models with different degrees of physiological detail, and the manner in which the effects of climatic factors are analyzed varies across that range of detail. Here we consider how well the current suite of gap models can accommodate climatic-change issues, and we suggest what physiological attributes and responses should be added to better represent responses of aboveground growth and competition. Whether a gap model is based on highly empirical, aggregated growth functions or more mechanistic expressions of carbon uptake and allocation, the greatest challenge will be to express allocation correctly. For example, incorporating effects of elevated CO₂ requires that the fixed allometry between stem volume and leaf area be made flexible. Simulation of the effects of climatic warming should incorporate the possibility of a longer growing season and acclimation of growth processes to changing temperature. To accommodate climatic-change factors, some of the simplicity of gap models must be sacrificed by increasing the amount of physiological detail, but it is important that the capability of the models to predict competition and successional dynamics not be sacrificed.     

Tomorrow’s Forests: Adapting to A Changing Climate

Today’s forests are largely viewed as a natural asset, growing in a climate envelope, which favors natural regeneration of species that have adapted and survived the variability’s of past climates. However, human-induced climate change, variability and extremes are no longer a theoretical concept. It is a real issue affecting all biological systems. Atmospheric scientists, using global climate models, have developed scenarios of the future climate that far exceed the traditional climate envelope and their associated forest management practices. Not all forests are alike, nor do they share the same adaptive life cycles, feedbacks and threats. Much of tomorrow’s forests will become farmed forests, managed in a pro-active, designed and adaptive envelope, to sustain multiple products, values and services. Given the life cycle of most forest species, forest management systems will need to radically adjust their limits of knowledge and adaptive strategies to initiate, enhance and plan forests in relative harmony with the future climate. Protected Areas (IUCN), Global Biosphere Reserves (UNESCO) and Smithsonian Institution sites provide an effective community-based platform to monitor changes in forest species, ecosystems and biodiversity under changing climatic conditions.     

Precipitation and temperature space–time variability and extremes in the Mediterranean region: evaluation of dynamical and statistical downscaling methods

This study evaluates how statistical and dynamical downscaling models as well as combined approach perform in retrieving the space–time variability of near-surface temperature and rainfall, as well as their extremes, over the whole Mediterranean region. The dynamical downscaling model used in this study is the Weather Research and Forecasting (WRF) model with varying land-surface models and resolutions (20 and 50 km) and the statistical tool is the Cumulative Distribution Function-transform (CDF-t). To achieve a spatially resolved downscaling over the Mediterranean basin, the European Climate Assessment and Dataset (ECA&D) gridded dataset is used for calibration and evaluation of the downscaling models. In the frame of HyMeX and MED-CORDEX international programs, the downscaling is performed on ERA-I reanalysis over the 1989–2008 period. The results show that despite local calibration, CDF-t produces more accurate spatial variability of near-surface temperature and rainfall with respect to ECA&D than WRF which solves the three-dimensional equation of conservation. This first suggests that at 20–50 km resolutions, these three-dimensional processes only weakly contribute to the local value of temperature and precipitation with respect to local one-dimensional processes. Calibration of CDF-t at each individual grid point is thus sufficient to reproduce accurately the spatial pattern. A second explanation is the use of gridded data such as ECA&D which smoothes in part the horizontal variability after data interpolation and damps the added value of dynamical downscaling. This explains partly the absence of added-value of the 2-stage downscaling approach which combines statistical and dynamical downscaling models. The temporal variability of statistically downscaled temperature and rainfall is finally strongly driven by the temporal variability of its forcing (here ERA-Interim or WRF simulations). CDF-t is thus efficient as a bias correction tool but does not show any added-value regarding the time variability of the downscaled field. Finally, the quality of the reference observation dataset is a key issue. Comparison of CDF-t calibrated with ECA&D dataset and WRF simulations to local measurements from weather stations not assimilated in ECA&D, shows that the temporal variability of the downscaled data with respect to the local observations is closer to the local measurements than to ECA&D data. This highlights the strong added-value of dynamical downscaling which improves the temporal variability of the atmospheric dynamics with regard to the driving model. This article highlights the benefits and inconveniences emerging from the use of both downscaling techniques for climate research. Our goal is to contribute to the discussion on the use of downscaling tools to assess the impact of climate change on regional scales.     

Assessment of future climate change over East Asia due to the RCP scenarios downscaled by GRIMs-RMP

This study assesses future climate change over East Asia using the Global/Regional Integrated Model system—Regional Model Program (RMP). The RMP is forced by two types of future climate scenarios produced by the Hadley Center Global Environmental Model version 2 (HG2); the representative concentration pathways (RCP) 4.5 and 8.5 scenarios for the intergovernmental panel on climate change fifth assessment report (AR5). Analyses for the current (1980–2005) climate are performed to evaluate the RMP’s ability to reproduce precipitation and temperature. Two different future (2006–2050) simulations are compared with the current climatology to investigate the climatic change over East Asia centered in Korea. The RMP satisfactorily reproduces the observed seasonal mean and variation of precipitation and temperature. The spatial distribution of the simulated large-scale features and precipitation by the RMP is generally less reflective of current climatic conditions than that is given by the HG2, but their inter-annual variations in East Asia are better captured by the RMP. Furthermore, the RMP shows higher reproducibility of climate extremes including excessive heat wave and precipitation events over South Korea. In the future, strong warming is distinctly coupled with intensified monsoonal precipitation over East Asia. In particular, extreme weather conditions are increased and intensified over South Korea as follows: (1) The frequency of heat wave events with temperature greater than 30 °C is projected to increase by 131 and 111 % in the RCP 8.5 and 4.5 downscaling, relative to the current climate. (2) The RCP 8.5 downscaling shows the frequency and variability of heavy rainfall to increase by 24 and 31.5 %, respectively, while the statistics given by the RCP 4.5 downscaling are similar to those of the current climate.     

Application of physical scaling towards downscaling climate model precipitation data

Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2–4 day), and long (more than 5-day) precipitation events is projected.     

Simulating the effects of climate changes on Eastern Eurasia forests

The extensive forests of Eastern Eurasia cover an area of ca. 6 million km². The FAREAST model, a forest gap model that simulates the stand composition and dynamics of Eastern Eurasian forests under the current climate, was used to simulate the responses of the Eastern Eurasia Forests to the climate change. Two different scenarios of possible future climatic change were obtained from the IPCC (2001) report (CMIP2 and IS92a-GS) and were used as input to the FAREAST model to determine the compositional and structural sensitivity to climate changes for several locations and along montane elevation gradients. The simulation results suggest that, under the influence of the conditions in the two climate-change scenarios, the underlying forest dynamics should be quite different. Further, Eastern Eurasian forests maintain currents forest structure and biomass only within a small range of climate change. Broad-leaved deciduous trees of such genera as Fraxinus, Quercus and Tilia increase their ranges over Eastern Eurasia under the climate-change scenarios. Conifers, such as Larix and Picea, decrease sharply under climate change and the area of their distributions are reduced. The overall biomass of Pinus is not decreased over the region. While the Pinus distribution range shifts, the area associated with the range of the taxa is not changed.     

Relationship between projected changes in future climatic suitability and demographic and functional traits of forest tree species in Spain

The response of plant species to future climate conditions is probably dependent on their ecological characteristics, including climatic niche, demographic rates and functional traits. Using forest inventory data from 27 dominant woody species in Spanish forests, we explore the relationships between species characteristics and projected changes in their average climatic suitability (occurrence of suitable climatic conditions for a species in a given territory) obtained by empirical niche-based models, under a business-as-usual climate change scenario (A1, HadCM3, 2001–2100). We hypothesize that most species will suffer a decline in climatic suitability, with a less severe for species (i) currently living in more arid climates or exhibiting a broader current climatic niche; (ii) with higher current growth rates; (iii) with functional traits related to resistance to water deficits. The analysis confirm our hypothesis since apart from a few Mediterranean species, most species decrease their climatic suitability in the region under future climate, characterized by increased aridity. Also, species living in warmer locations or under a wider range of climatic conditions tend to experience less decrease in climatic suitability. As hypothesized, a positive relationship was detected between current relative growth rates and increase in future climatic suitability. Nevertheless, current tree mortality did not correlate with changes in future climatic suitability. In contrast with our hypothesis, functional traits did not show a clear relationship with changes in climate suitability; instead species often presented idiosyncratic responses that, in some cases, could reflect past management. These results suggest that the extrapolation of species performance to future climatic scenarios based on current patterns of dominance is constrained by factors other than species autoecology, particularly human activity.     

Temporal statistical downscaling of precipitation and temperature forecasts using a stochastic weather generator

Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society(IRI). In conjunction with the GLM(generalized linear modeling) weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts(the IRI format only specifies probabilities for three categories: below normal, near normal, and above normal) into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.     

A comparison of forest gap models: Model structure and behaviour

Forest gap models share a common structure for simulating tree population dynamics, and many models contain the same or quite similar ecological factors. However, a wide variety of formulations are being used to implement this general structure. The comparison of models incorporating different formulations is important for model validation, for assessing the reliability of model projections obtained under scenarios of climatic change, and for the development of models with a wide range of applicability. This paper reviews qualitative and quantitative comparisons of the structure and behaviour of forest gap models.As examples of qualitative model comparisons, the different formulations used for the heightdiameter relationship, for the maximum growth equation, and for the effects of temperature and drought on tree growth are reviewed. The variety of formulations currently in use has the potential to influence simulation results considerably, but we conclude that little is known on the sensitivity of the models in this respect.The quantitative model comparisons performed so far allow us to draw the following conclusions: (1) Gap models are quite sensitive to the formulation of climate-dependent processes under current climate, and this sensitivity is even more pronounced under a changed climate. (2) Adaptations of forest gap models to specific regions have required detailed sub-models of species life history, thus complicating model comparison. (3) Some of the complex models developed for region-specific applications can be simplified without hampering the realism with which they simulate species composition. (4) Attempts to apply the models without modification beyond the area for which they were developed have produced controversial results.It is concluded that the sensitivity of forest gap models to the exact process formulations should be examined carefully, and that more systematic comparisons of model behaviour at a range of test sites would be desirable. Such studies could improve our understanding of forest dynamics considerably, and they would help to focus future research activities with gap models.     

Evaluation of machine learning tools as a statistical downscaling tool: temperatures projections for multi-stations for Thames River Basin, Canada

Many impact studies require climate change information at a finer resolution than that provided by global climate models (GCMs). This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely single conjunctive rule learner, decision table, M5 model tree, and REPTree, and explores the impact of climate change on maximum and minimum temperatures (i.e., predictands) of 14 meteorological stations in the Upper Thames River Basin, Ontario, Canada. The data used for evaluation were large-scale predictor variables, extracted from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset and the simulations from third generation Canadian coupled global climate model. Data for four grid points covering the study region were used for developing the downscaling model. M5 model tree algorithm was found to yield better performance among all other learning techniques explored in the present study. Hence, this technique was applied to project predictands generated from GCM using three scenarios (A1B, A2, and B1) for the periods (2046–2065 and 2081–2100). A simple multiplicative shift was used for correcting predictand values. The potential of the downscaling models in simulating predictands was evaluated, and downscaling results reveal that the proposed downscaling model can reproduce local daily predictands from large-scale weather variables. Trend of projected maximum and minimum temperatures was studied for historical as well as downscaled values using GCM and scenario uncertainty. There is likely an increasing trend for T _max and T _min for A1B, A2, and B1 scenarios while decreasing trend has been observed for B1 scenarios during 2081–2100.     

Assessing potential impacts of climatic change on subalpine forests on the eastern Tibetan Plateau

Forest gap models have been used widely in the study of forest dynamics, including predicting long-term succession patterns and assessing the potential impacts of climate change on forest structure and composition. However, little effort is devoted to predict forest dynamics in the high elevation areas, although they have the sensitive response to global climate change. In the present study, based on a modified height-diameter function, we developed a new version (FAREAST-GFSM) of the forest patch model, FAREAST for simulating the changes of subalpine forests. The observed data from the Gongga Mt. Alpine Station were also used to test model precision. With the improved performance of FAREAST-GFSM, we explored the impact of three warming scenarios on subalpine forest on the eastern Tibetan plateau within a 100-year period. The study result indicates that the effects of climate change were evident on subalpine forests in the high elevation areas. The response of different species to the warming climate might eventually transform the subalpine Abies fabric forest into Betula utilis forest similar to that which is now widely distributed in the eastern Tibetan Plateau mountainous areas with the relatively lower elevation. Subalpine forests could move to higher and colder areas, which are currently tundra.