Climate change impact and potential adaptation strategies under alternate realizations of climate scenarios for three major crops in Europe
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This study presents an estimate of the effects of climate variables and CO2 on three major crops, namely wheat, rapeseed and sunflower, in EU27 Member States. We also investigated some technical adaptation options which could offset climate change impacts. The time-slices 2000, 2020 and 2030 were chosen to represent the baseline and future climate, respectively. Furthermore, two realizations within the A1B emission scenario proposed by the Special Report on Emissions Scenarios (SRES), from the ECHAM5 and HadCM3 GCM, were selected. A time series of 30 years for each GCM and time slice were used as input weather data for simulation. The time series were generated with a stochastic weather generator trained over GCM-RCM time series (downscaled simulations from the ENSEMBLES project which were statistically bias-corrected prior to the use of the weather generator). GCM-RCM simulations differed primarily for rainfall patterns across Europe, whereas the temperature increase was similar in the time horizons considered. Simulations based on the model CropSyst v. 3 were used to estimate crop responses; CropSyst was re-implemented in the modelling framework BioMA. The results presented in this paper refer to abstraction of crop growth with respect to its production system, and consider growth as limited by weather and soil water. How crop growth responds to CO2 concentrations; pests, diseases, and nutrients limitations were not accounted for in simulations. The results show primarily that different realization of the emission scenario lead to noticeably different crop performance projections in the same time slice. Simple adaptation techniques such as changing sowing dates and the use of different varieties, the latter in terms of duration of the crop cycle, may be effective in alleviating the adverse effects of climate change in most areas, although response to best adaptation (within the techniques tested) differed across crops. Although a negative impact of climate scenarios is evident in most areas, the combination of rainfall patterns and increased photosynthesis efficiency due to CO2 concentrations showed possible improvements of production patterns in some areas, including Southern Europe. The uncertainty deriving from GCM realizations with respect to rainfall suggests that articulated and detailed testing of adaptation techniques would be redundant. Using ensemble simulations would allow for the identification of areas where adaptation, like those simulated, may be run autonomously by farmers, hence not requiring specific intervention in terms of support policies.Keywords:
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Abstract Two statistical downscaling models were developed for downscaling monthly General Circulation Model ( GCM ) outputs to precipitation at a site in north‐western Victoria, Australia. The first downscaling model was calibrated and validated with the National Centers for Environmental Prediction/National Center for Atmospheric Research ( NCEP/NCAR ) reanalysis outputs over the periods of 1950–1989 and 1990–2010 respectively. The projections of precipitation into the future were produced by introducing the outputs of HadCM3, ECHAM5 , GFDL2.0 and GFDL2.1 , pertaining to A2 and B1 greenhouse gas emission scenarios to this downscaling model. In this model, the input data used in the development and future projections are not homogeneous, as they originate from two different sources. As a solution to this issue, the second downscaling model was developed and precipitation projections into the future were produced with a homogeneous set of inputs. To produce a homogeneous set of inputs to this model, regression relationships were formulated between the NCEP/NCAR reanalysis outputs and the twentieth‐century climate experiment outputs corresponding to the variables used in the first downscaling model obtained from the ensemble consisting of HadCM3, ECHAM5 and GFDL2.0 . The outputs of these relationships pertaining to the periods of 1950–1989 and 1990–1999 were used for the calibration and validation of this downscaling model respectively. Using the outputs of HadCM3, ECHAM5 and GFDL2.0 pertaining to A2 and B1 emission scenarios on these relationships, inputs for the second downscaling model pertaining to the period of 2000–2099 were generated. The first downscaling model with NCEP/NCAR reanalysis outputs showed a high Nash–Sutcliffe Efficiency ( NSE ) of 0.75 over the period 1950–1999. When this downscaling model was run with the twentieth‐century climate experiment outputs of HadCM3, ECHAM5 , GFDL2.0 and GFDL2.1 , it exhibited limited performances over the period 1950–1999, which was indicated by relatively low NSEs of −0.62, −2.54, −0.40 and −0.48 respectively. The second downscaling model displayed an NSE of 0.35 over the period 1950–1999.
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Abstract In the 21st century, Climate change has become one of the prominent global challenges which threats the world, and the changes in climate extremes are estimated to have catastrophic consequences on human society and the natural environment. To overcome the spatial-temporal inadequacy of the GCMs, Linking large-scale General Circulation Model (GCM) data with small-scale local climatic data highly comes to the fore. In this paper, two statistical downscaling techniques encompass LARS-WG and SDSM were employed for assessing the fluctuations of temperature predictand in Tabriz city, Iran. In order to choose the well-response GCMs a Multi-GCM ensemble approach was utilized by EC-EARTH, HadCM2, MIROC5, MPI-ESM GCMs from the CMIP5. To study the impact of climate change over the region, the periods of 1961-1990 and 1991-2005 were used as the baseline and validation period, respectively. Results of evaluation criteria disclosed the superior performance of Multi-GCM ensemble for predicting temperature predictand compared to single GCM models. Furthermore, the result of climate projection for the temperature predictand by both models revealed that the city will experience an increasing trend in temperatures for the horizon of 2021-2080. The average temperature will increase by 2.9 and 3.7 (°C) under Representative Concentration Pathways (RCPs) (i.e., RCP4.5 and 8.5), respectively.
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The climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, General Circulation Models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. The results presented in this thesis have indicated that it is feasible to link large-scale atmospheric variables by GCM simulations from Hadley Centre 3rd generation (HadCM3) outputs with daily precipitation at a local site. Statistical Downscaling Model (SDSM) was applied using three set of data; daily precipitation data for the period 1961-1990 corresponding to Endau rainfall (Station no. 2536168) and Muar (Station no. 2228016) located in Johor at the Southern region of Peninsular Malaysia; The observed daily data of large-scale predictor variables derived from the National Centre for Environmental Prediction (NCEP) and GCM simulations from Hadley Centre 3rd generation (HadCM3). The HadCM3 data from 1961 to 2099 were extracted for 30-year time slices. The result clearly shows increasing increment of daily mean precipitation of most of the months within a year in comparison to current 1961-1990 to future projections 2020’s, 2050’s and 2080’s considering SRES A2 and B2 scenarios developed by the Intergovernmental Panel on Climate Change (IPCC). Frequency analysis techniques were carried out using the observed annual daily maximum precipitation for period 1961-1990 and downscaled future periods 2020’s, 2050’s and 2080’s. Therefore, it does appear that SDSM can be considered as a bench mark model to interpret the impact of climate change.
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Prediction of Rainfall under HadCM3 and CanESM2 Climate Change Models using Statistical Downscaling Model (Case Study: Tabriz Synoptic Station)
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The knowledge of future climate information at local level has enormous advantage in Ethiopia, where the driver of the economy is agriculture. This study was conducted to downscale the climate change scenarios for Miesso station for the year 2011-2099. Daily climate data and normalized large scale Hadley Centre coupled Model version 3 (HadCM3) model predictors were used for downscaling climate change scenarios. The change for rainfall, minimum and maximum temperatures were developed using the HadCM3 A2a and B2a Emission Scenarios by Statistical Downscaling Model (SDSM) version 4.1software. The SDSM analysis showed an increasing trend for both annual precipitation and temperatures. Accordingly, the average monthly and annual minimum and maximum temperatures were found to rise in 2020, 2050 and 2080s for A2a and B2a emission scenarios. Nevertheless in 2080s, the average annual maximum temperature increment would be high for both A2a and B2a scenarios. Therefore the use of seasonal climate outlook information and introduction of new crops, varieties and management practices that goes in line with the changing climate patterns is suggested for the study area.
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There are statistical downscaling methods such as: SDSM, LARS-WG, WGEN…, used to convert information on climate variables from the simulation results of General Circulation Model (GCM) to build climate change scenarios for local region. In this study, we used the LARS-WG model and HadCM3 GCM for two emission scenarios: B1 (low emission scenario) and A1B (medium emission scenario) to generate future scenarios for temperature and precipitation at meteorological stations and rain gauges in the Srepok watershed. The LARS-WG model was calibrated and validated against observed climate data for the period 1980-2009, and the calibrated LARS-WG was then used to generate future climate variables for the 2020s (2011-2030), 2055s (2046-2065), and 2090s (2080-2099). The climate change scenarios suggested that the climate in the study area will become warmer and drier in the future. The results obtained in this study could be useful for policy makers in planning climate change adaptation strategies for the study area.
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In the present study SDSM downscaling model was used as a tool for downscaling weather data statistically in upper Godavari river basin. Two Global Climate Models (GCMs), CGCM3 and HadCM3, have been used to project future maximum temperature (Tmax), minimum temperature (Tmin) and precipitation. The predictor variables are extracted from: 1) the National Centre for Environmental Prediction (NCEP) reanalysis dataset for the period 1961-2003, 2) the simulations from the third-generation Hadlycentre Coupled Climate Model (HadCM3) and Coupled Global Climate Model (CGCM3) variability and changes in Tmax, Tmin and precipitation under scenarios A1B and A2 of CGCM3 model and A2 and B2 of HadCM3 model have been presented for future periods: 2020s, 2050s and 2080s. The scatter-plots and cross-correlations are used for verifying the reliability of the simulation. Maximum temperature increases in future for almost all the scenarios for both GCMs. Also downscaled future precipitation shows increasing trends for all scenarios.
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General circulation models (GCMs) have been employed by climate agencies to predict future climate change. A challenging issue with GCM output for local relevance is their coarse spatial resolution of the projected variables. Statistical Downscaling Model (SDSM) identifies relationships between large-scale predictors (i.e., GCM-based) and local-scale predictands using multiple linear regression models. In this study (SDSM) was applied to downscale rainfall and temperature from GCMs. The data from single station located in the Indira Sagar canal command area at Madhya Pradesh, India were used as input of the SDSM. The study included calibration and validation with large-scale atmospheric variables encompassing the NCEP reanalysis data, the future estimation due to a climate scenario, which is HadCM3 A2. Results of the downscaling experiment demonstrate that during the calibration and validation stages, the SDSM model can be well acceptable regard its performance in the downscaling of daily rainfall and temperature. For a future period (2010-2099), the SDSM model estimated an increase in total average annual rainfall and annual average temperature for station. This indicates that the area of station considered will be wet and humid in the future. Also, the mean temperature is projected to rise to 1.5 C to 2.5 C for present study area. However, the model projections show a rise in mean daily precipitation with varying percentage in the months of July (0.59% to 2.09%) and August (0.79% to 1.19) under A2 of HadCM3 model for future periods.
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Future rainfall projection can be predicted by using Global Climate Model (GCM). In spite of low resolution, we are not able specifically to describe a local or regional information. Therefore, we applied downscaling technique of GCM output using Regional Climate Model (RCM). In this case, Regional Climate Model version 3 (RegCM3) is used to accomplish this purpose. RegCM3 is regional climate model which atmospheric properties are calculated by solving equations of motion and thermodynamics. Thus, RegCM3 is also called as dynamic downscaling model. RegCM3 has reliable capability to evaluate local or regional climate in high spatial resolution up to 10 × 10 km. In this study, dynamically downscaling techniques was applied to produce high spatial resolution (20 × 20 km) from GCM EH5OM output which commonly has rough spatial resolution (1.875 o × 1.875 o ). Simulation show that future rainfall in Indramayu is relatively decreased compared to the baseline condition. Decreased rainfall generally occurs during the dry season (July-June-August/JJA) in a range 10-20%. Study of extreme daily rainfall indicates that there is no significant increase or decrease value.
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