General Circulation Model Data Research Articles

Assessing the impact of climate change over the northwest of Iran: an overview of statistical downscaling methods

Due to the spatial-temporal inadequacy of large-scale general circulation models (GCMs), linking large-scale GCM data with small-scale local climatic data has found great interest. In this paper, in order to downscale minimum and maximum temperatures and precipitation predictands, the performance of three statistical downscaling techniques including Long Ashton Research Station-Weather Generator (LARS-WG), statistical downscaling model (SDSM), and artificial neural network (ANN) was compared based on Intergovernmental Panel on Climate Change-Fifth Assessment Report (IPCC-AR5) in northwest Iran. For this purpose, a nonparametric test named Mann-Whitney test, Spearman correlation coefficient, and the root mean square error (RMSE) were utilized to assess the efficiency of downscaling models. To scrutinize the climate change impacts, periods of 1961–1990 and 1991–2005 were considered as the baseline and verification periods, respectively. The findings revealed the superior performance of the ANN model for minimum and maximum temperatures, while for precipitation predictand, the SDSM represented the best performance among the models. Simulation results for future temperature indicated an ascending trend as 0.1–1.3 °C, 0.3–1.7 °C, and 0.5–2.1 °C for LARS-WG, SDSM, and ANN techniques, respectively. On the other hand, simulation outputs for the precipitation indicated a descending trend of 10–30% in future precipitation of the region according to downscaling models under Representative Concentration Pathway 8.5 (RCP8.5) pessimistic scenario of Hadley Center Coupled Model version 3 (HadCM3) GCM model.

Statistical downscaling of GCM using kernel support vector regression for rainfall prediction in Bireuen district

Global climate phenomena, including El-Nino and Southern Oscillation, Indian Ocean Dipole and Madden Julian Oscillation, have affected rainfall behavior in Indonesia. This study attempts to model a statistical downscaling of the General Circulation Model (GCM), which is usually used as learning data for the prediction of climate change, using Support Vector Regression (SVR) to predict rainfall during the dry season in Bireuen, Aceh Province. Data consists of observational rainfall data at 7 (seven) weather stations and 10 models of 7x7grid-scale hindcasts of GCM data collected within 24 years (1990-2013). The RBF kernel was found to produce the best performance (correlation value: 0.828; RMSE: 24.035) compared to the linear (correlation value: 0.538; RMSE: 27.207) and polynomial kernel (correlation value: 0.639; RMSE: 25.584). Among GCM downscaling output, CMC1-CanCM3 was considered to be the best model for rainfall prediction in Bireun District, particularly in Peudada and Gandapura regions. This model is recommended to be used as a rainfall forecasting tool during drought and would help in optimizing the arrangement of cropping strategy in Bireun area to prevent possible crop failure.

Incorporation of Long Term Climate Changes in Hydrological Modelling

One of climate change's most important concerns at the moment is its impact on hydrology as it has direct links with agriculture, vegetation, and livelihood. This study tries to analyze potential future climate change in the Kumaradhara river basin. This study involved three steps: (1) acquiring and using general circulation model (GCM) to project future global climate scenarios; (2) establishing statistical relationships between GCM data and observed data using Statistical Downscaling Model (SDSM); (3) downscaling the second generation Canadian Earth system Model (CanESM2)GCM output based on the established statistical relationship. The statistical downscaling is carried out for three scenarios used in the fifth evaluation report of the recent Intergovernmental Panel on Climate Change (IPCC) viz., Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5. The statistical downscaling Model (SDSM) results showed that the mean annual daily precipitation is altered in the basin under all the scenarios but it will be different in different time periods depending on scenarios and the basin will experience the reduced precipitation levels in summer. Also the precipitation will marginally rise in all the time slices with reference to baseline data. We can conclude from the results that this region's climate will affect future farming as the availability of water is bound to change. This study should, however, be followed up by a larger study incorporating multiple CMIP5 models such that changes in hydrological-regimes can be examined appropriately.

Shifts Along a Spectrum: A Longitudinal Study of the Western Eurasian Realized Climate Niche

ABSTRACT Climate niches that modern humans and earlier hominin ancestors occupied have changed dramatically over time, but the extent of those changes has gone largely undocumented. This study investigates the manner in which the realised hominin climate niche has expanded, contracted, or stayed stationary across four time periods (Last Interglacial, Last Glacial Maximum, Mid-Holocene, and 1950–2000) in Western Eurasia. Using spatially gridded general circulation model data and site locations this study examines climate variables from archaeological sites and current Western Eurasian cities to describe both the regional Western Eurasian fundamental and realised climate niches. Changes between the three prehistoric periods and modern-day time period are analysed by calculating each realised niche breadth, overlap, position, and variance. Results indicate that as global temperatures cooled from the Last Interglacial to Last Glacial Maximum, populations expanded their climate niche breadth beyond that of earlier Neanderthal groups, shifting toward regions with less seasonal variation. Conversely, Mid-Holocene humans, who saw the proliferation of both agriculture and population, contracted their realised climate niche space. The contraction and expansion of realised climate niche space illustrates how hominins have evolved the capacity to shift their niche through changes to their subsistence strategy and adaptations to overall climatic conditions.

Impacts of Climate Change and Land-Use Change on Hydrological Extremes in the Jinsha River Basin

Hydrological extremes are closely related to extreme hydrological events, which have been and continue to be one of the most important natural hazards causing great damage to lives and properties. As two of the main factors affecting the hydrological cycle, land-use change and climate change have attracted the attention of many researchers in recent years. However, there are few studies that comprehensively consider the impacts of land-use change and climate change on hydrological extremes, and few researchers have made a quantitative distinction between them. Regarding this problem, this study aims to quantitatively distinguish the effects of land-use change and climate change on hydrological extremes during the past half century using the method of scenarios simulation with the soil and water assessment tool (SWAT). Furthermore, the variations of hydrological extremes are forecast under future scenarios by incorporating the downscaled climate simulations from several representative general circulation models (GCMs). Results show that: (1) respectively rising and declining risks of floods and droughts are detected during 1960–2017. The land use changed little during 1980–2015, except for the water body and building land. (2) The SWAT model possesses better simulation effects on high flows compared with low flows. Besides, the downscaled GCM data can simulate the mean values of runoff well, and acceptable simulation effects are achieved for the extreme runoff indicators, with the exception of frequency and durations of floods and extreme low flows. (3) During the period 1970–2017, the land-use change exerts little impact on runoff extremes, while climate change is one of the main factors leading to changes in extreme hydrological situation. (4) In the context of global climate change, the indicators of 3-day max and 3-day min runoff will probably increase in the near future (2021–2050) compared with the historical period (1970–2005). This research helps us to better meet the challenge of probably increased flood risks by providing references to the decision making of prevention and mitigation measures, and thus possesses significant social and economic value.

A Temperature-Scaling Approach for Projecting Changes in Short Duration Rainfall Extremes from GCM Data

Current and future urban flooding is influenced by changes in short-duration rainfall intensities. Conventional approaches to projecting rainfall extremes are based on precipitation projections taken from General Circulation Models (GCM) or Regional Climate Models (RCM). However, these and more complex and reliable climate simulations are not yet available for many locations around the world. In this work, we test an approach that projects future rainfall extremes by scaling the empirical relation between dew-point temperature and hourly rainfall and projected changes in dew-point temperature from the EC-Earth GCM. These projections are developed for the RCP 8.5 scenario and are applied to a case study in the Netherlands. The shift in intensity-duration-frequency (IDF) curves shows that a 100-year (hourly) rainfall event today could become a 73-year event (GCM), but could become as frequent as a 30-year (temperature-scaling) in the period 2071–2100. While more advanced methods can help to further constrain future changes in rainfall extremes, the temperature-scaling approach can be of use in practical applications in urban flood risk and design studies for locations where no high-resolution precipitation projections are available.

Extreme Rainfall Prediction using Bayesian Quantile Regression in Statistical Downscaling Modeling

Statistical Downscaling (SD) is a model that uses satellite data from General Circulation Models (GCM), which in climatology are very useful in predicting climate for the next few decades. GCM data is generally ill-conditioned, which is high dimension and multicollinearity, so a special technique is needed to handle this poorly conditioned. One of the variable selection techniques and handling of multicollinearity which is currently highly developed is regularization techniques including Adaptive Lasso, where selective parameters are adaptive, which can differ for each regression coefficient. Until now, predictions of extreme rainfall in Indonesia have not used Adaptive Lasso in SD modeling. This paper aims to predict the amount of rainfall (in millimeters) at moderate extreme (quantile 0.75) and high extreme rainfall (quantile 0.9 da 0.95) and handling poorly conditioned GCM data with Adaptive Lasso techniques and building predictive models of local rainfall by utilizing GCM data using the Bayes quantile regression model. Response in the form of monthly rainfall at Indramayu district, West Java Indonesia, and 49 explanatory variables in the form of GCM precipitation data in the period January 1981 - December 2013, which handled multicollinearity and variable selection using the Adaptive Lasso. The results are very satisfying with correlation between predicted and real data is above 0.91 for and the RMSEP is less than 50.

Model Statistical Downscaling Nonparametrik pada Simulasi Data Curah Hujan Harian Pos Jurang Malang Daerah Aliran Sungai Jangkok

The prediction of river water discharge can be determined by developing a river water discharge model based on climate information, especially rainfall information. This research aims to obtain a simulation of rainfall data that will be used to river water discharge modeling. The simulation of rainfall data is obtained using statistical downscaling model which develop the functional model between global climate data and local climate data. Daily precipitation of General Circulation Model (GCM) is used to be predictor variables. It is global climate data.. And, daily rainfall of Jangkok watershed, the local climate data, is used to be response variable.. In order to reduce the dimension of GCM global data, GCM data is projected to a litle number of variable using classification and regression tree (CART) method. Then, the projection variables are used to develop statistical downscaling model of rainfall based on Kernel nonparametric regression. Daily rainfall data of Jurang Malang station, Jangkok watershed, is simulated based on the model obtained.

Analysis of Observed and Modelled Near-Surface Wind Extremes over the Sub-Arctic Northeast Pacific

Wind speed extremes in the sub-Arctic realm of the North-East Pacific region were investigated through extreme value analysis of wind speed obtained from wind simulations of the COSMO-CLM (Consortium for Small-scale Modelling, climate version) mesoscale model, as well as using observed data. The analysis showed that the set of wind speed extremes obtained from observations is a mixture of two different subsets each neatly described by the Weibull distribution. Using special metaphoric terminology, they are labelled as “Black Swans” and “Dragons”. The “Dragons” are responsible for strongest extremes. It has been shown that both reanalysis and GCM (general circulation model) data have no “Dragons”. This means that such models underestimate wind speed maxima, and the important circulation process generating the anomalies is not simulated. The COSMO-CLM data have both “Black Swans” and “Dragons”. This evidence provides a clue that an atmospheric model with a detailed spatial resolution (we used in this work the data from domain with 13.2 km spatial resolution) does reproduce the special mechanism responsible for the generation of the largest wind speed extremes. However, a more thorough analysis shows that the differences in the parameters of the cumulative distribution functions are still significant. The ratio between the modelled Dragons and Black Swans can reach up to only 10%. It is much less than 30%, which was the level established for observations.

Assessing the contribution of different uncertainty sources in streamflow projections

Hydrological models are commonly used to quantify the hydrological impacts of climate change using general circulation model (GCM) simulations as input. However, application of the model results with respect to future changes in streamflow scenarios remains limited by the large uncertainties stemming from various sources. Therefore, this study aimed to explore uncertainties involved in climate change impact assessment in Hulu Langat Basin, Malaysia, and define the contribution of uncertainty sources to the final uncertainty level. Hydrological model parameters, GCMs, and emission scenario uncertainties were considered the main uncertainty contributors in local-scale impact studies. The equidistant quantile matching method is used to bias-correct simulations of 19 GCMs under two emission scenarios of RCP4.5 and RCP8.5. The Soil and Water Assessment Tool (SWAT) hydrological model is next run by the bias-corrected GCM data to generate a wide spectrum of future streamflow scenarios. Projected monthly streamflow pattern under RCP8.5 showed a different temporal pattern from the observed one. Hydrological model parameter uncertainty was proven to be a larger uncertainty contributor than emission scenario during baseline climate. GCM and emission scenario uncertainties escalated as progressed in time and GCM uncertainty showed larger increments. The monthly pattern of effect of each uncertainty source varied when comparing the two periods of 2030s and 2080s. Therefore, for a superior management of water resources, a study of climate change impacts and uncertainty sources on a smaller scale than the decadal or annual scales can be more informative to the decision makers.

Climate change and the building sector: Modelling and energy implications to an office building in southern Europe

The building sector is one of the most relevant sectors in terms of generation of wealth and occupation, but it is also one of the main contributor to energy use and greenhouse gas emissions (e.g. at the European level it is currently responsible for 36% of CO2 emissions). For these reason this sector must play a key role in achieving a low-carbon economy consistent with the objective of holding the increase of the average temperature of the globe below 2 °C if compared to pre-industrial levels.In this context, the paper analyses the potential impact of climate change on the energy uses for heating and cooling in southern Europe, based on the assumptions of the latest Intergovernmental Panel on Climate Change (IPCC) future climate projections (assessment report 5). Different General Circulation Models (GCMs) were analysed using different metrics for selecting the most suitable one to be applied to building simulation. GCM data were used as input to a downscaling method known as “morphing”, to generate hourly weather files for 3 future time projections (2035, 2065 and 2090). Finally, in order to assess the building energy use for heating and cooling for the next century, energy simulations for a case study were performed.The results show, in all scenarios, consistent and large increases in future air temperature. The impacts of these driving forces on heating and cooling energy use are very relevant: the results show an overall increase in total energy consumption with a relative decrease in heating demand and increase in cooling demand: the yearly heating and cooling energy requirement in 2090 is expected to increase in a range of +50.8–119.7% if measures are not foreseen to counter and limit the effects of climate change.

Modeling uncertainty of statistical downscaling methods in quantifying the climate change impacts on late spring frost risk over Iran

Late spring frosts (LSFs) play a key role in the evaluation of climate suitability for agricultural and horticultural crop production and are considered to be one of the main components of food security. It is expected that future climate change will affect the occurrence of LSFs and its damages. In order to quantify these changes, this study aims to investigate the performance of two downscaling methods (SDSM and ANN), using two GCMs (general circulation model) data of HadCM3 (A2 and B2 scenarios) and CGCM3 (A1B and A2 scenarios). To this end, daily minimum temperature data (Tmin i ) of 50 meteorological stations located across the entire country were gathered and quality controlled for the period 1961 through 2010. The trend analyses of annual minimum temperature series showed a significant increasing trend of 0.3 °C per decade (α = 0.01). Both downscaling models were calibrated for the 40 years of 1961–2000 and the evaluation is conducted for the 10 years of 2001–2010. The Wilcoxon rank-sum and the Levene and bootstrapping tests were used for comparing and finding confidence intervals of averages and variances of downscaled daily minimum temperatures at each month. Results showed that, firstly, downscaling models’ performance in simulating averages is much better than variances. Secondly, SDSM simulates warmer summers and colder winters comparing ANN. Thirdly, the best and the worst results were achieved by ANN_CGCM3_A2 and ANN_HadCM3_B2, respectively. Finally, very low, moderate, high, and very high-risk zones for critical temperature of 0 °C and their areas at the historical period (1961–2010) and three future periods (2011–2040, 2041–2070, 2071–2100) were quantified and compared. Based on the results, SDSM simulated changes in the high and low frost risk zones more than the ANN model. However, since both Very High and No Frost risk zones will drastically change, SDSM results do not always indicate an increasing risk of frost damage. Moreover, the average of the area covered by “low-risk” zone will increase from 1.00% in the current period to 4.43% at the end of the century, and area of “very high-risk” zone will decrease from 8.2 to 4.7% at the same condition.

The physical processes causing nocturnal rainfall over northwest Australia and their representation in high‐ and low‐resolution models with parametrized convection

The diurnal cycle of precipitation in the Tropics is represented poorly in general circulation models (GCMs), which is primarily attributed to the representation of moist convection. Nonetheless, in areas where precipitation is driven by the diurnal cycle in the synoptic‐scale flow, GCMs may represent that circulation–rainfall relationship well. Over northwest Australia there is a tendency for precipitation to peak overnight where the diurnal cycle of the heat low circulation leads to the development of strong convergence after local sunset. In order to assess the heat low–precipitation relationship in more detail, a case‐study approach is used to investigate the actual ‘weather’ that is responsible for night‐time precipitation. The study shows that, where there is sufficient moisture, precipitation typically forms along convergence zones that coincide with boundaries between relatively moist and dry air masses (termed a ‘dryline’). A convergence line detection algorithm is then used to identify the fraction of observed nocturnal rainfall that is associated with any convergence zones. The same evaluation is then undertaken for a relatively high‐resolution (MetUM) and low‐resolution (ACCESS1.0) GCM, which simulate rainfall‐generation processes similar to the observations. Finally, the convergence line detection/precipitation algorithm is run on other GCM data (from CMIP5) to see whether the same processes occur despite different model configurations (i.e. physics), which appears to be the case.

Climate change and vine protection : the case of mildews management in Burgundy

Viticulture worldwide is currently facing two major challenges: adapting to climate change and reducing its environmental footprint. Plant protection is a central aspect of these challenges, firstly because pests and diseases development is strongly controlled by climate conditions, and secondly, because viticulture requires in many regions large quantities of pesticides. Phytosanitary protection is even more crucial for terroir-based viticulture areas, because the negative image given by excessive pesticide use impacts the whole region which reputation is partly built on environmental friendly practices. Moreover, most of terroir wines sensory properties and fame rely on specific cultivars, which makes it difficult to replace them using diseases resistant varieties. This study addresses the potential impact of climate change on pesticide use to control powdery and downy mildew in Burgundy. To assess the past evolution of diseases risk, a database composed by yearly number of applications of phytosanitary treatments for powdery and downy mildew diseases was built. This information was collected from 400 grapevine growers originating from 5 sub-regions of Burgundy. The data refer to yearly average number of treatments during the 1995-2014 period. Pesticide applications was related to climate by means of multiple linear regression models between the average number of treatments for powdery and downy mildews control and monthly temperature and monthly rainfall indices from April to July. Models providing the lowest error (estimated trough leave-one-out cross-validation) were selected for each of the 5 Burgundy wine sub-regions. According to each region, mildews yearly treatments number were significantly related to monthly climate data. In most models, May and June average temperature were selected with negative regression coefficients while April and May monthly precipitation were selected with positive regression coefficients. These models were fed with 1980-2100 temperature and rainfall projected data using CMIP5 RCP8.5 scenario from 18 GCM (General Circulation Models) statistically downscaled and debiased to match a daily 12 km target resolution. Increasing temperature and a slight ensemble rainfall increase (depending on the GCM) simulated during spring and early summer had inverse effects on models trend. However, regression models project a decreasing trend of the number of treatments for mildews control along the 21st century, based upon GCM data. Spraying numbers are expected to slightly vary in the future (-4,8 to -34.1% reduction), which strengthens the need for alternative plant protection strategies to match both consumer and policies promoting lower pesticides use for viticulture.

Framework for assessment of climate change impact on offshore wind energy

ABSTRACTA probabilistic framework for evaluating changes in offshore wind potential under a changing climate is presented considering three general circulation models (GCMs), two representative concentration pathways and two statistical downscaling techniques. The analysis was done with respect to three specific locations along the Indian coastline, identified earlier for future offshore energy extraction. The uncertainty among the GCMs was addressed and the choice of an appropriate GCM was made by combining analysis of GCM uncertainty and model reliability. The regional scale wind was derived from the selected GCM using one hard computing method, namely bilinear interpolation and quantile mapping, and one soft method, an artificial neural network. The efficiency of these methods was assessed using certain performance criteria for the historical period and a convergence criterion over the future period. To evaluate wind potential, the GCM output was downscaled from daily to hourly, taking into account the removal of gusts because of long interval averaging. The wind potential for past (1979–2005) and future (2006–2032) time slices was determined using both hard and soft methods as well as two representative concentration pathways. The resulting four outcomes at annual and seasonal scale were compared with the extrapolated trend of past wind potential derived from both GCM and reanalysis data. Finally, the most efficient strategy was evaluated and the actual extractable power was estimated considering a standard power curve. It is concluded that at all the three locations the annual average wind potential will substantially increase, benefitting the offshore industry.

High resolution decadal precipitation predictions over the continental United States for impacts assessment

Unprecedented alterations in precipitation characteristics over the last century and especially in the last two decades have posed serious socio-economic problems to society in terms of hydro-meteorological extremes, in particular flooding and droughts. The origin of these alterations has its roots in changing climatic conditions; however, its threatening implications can only be dealt with through meticulous planning that is based on realistic and skillful decadal precipitation predictions (DPPs). Skillful DPPs represent a very challenging prospect because of the complexities associated with precipitation predictions. Because of the limited skill and coarse spatial resolution, the DPPs provided by General Circulation Models (GCMs) fail to be directly applicable for impact assessment. Here, we focus on nine GCMs and quantify the seasonally and regionally averaged skill in DPPs over the continental United States. We address the problems pertaining to the limited skill and resolution by applying linear and kernel regression-based statistical downscaling approaches. For both the approaches, statistical relationships established over the calibration period (1961–1990) are applied to the retrospective and near future decadal predictions by GCMs to obtain DPPs at ∼4km resolution. The skill is quantified across different metrics that evaluate potential skill, biases, long-term statistical properties, and uncertainty. Both the statistical approaches show improvements with respect to the raw GCM data, particularly in terms of the long-term statistical properties and uncertainty, irrespective of lead time. The outcome of the study is monthly DPPs from nine GCMs with 4-km spatial resolution, which can be used as a key input for impacts assessments.

An alternative method for predicting relative humidity for climate change studies

The reliable prediction of climate variables at finer temporal resolutions, e.g. daily scale, is essential for climate change impact analysis. However, humidity variables remain less focused, although probable changes under changing climate conditions and their effect on impact studies can be significant. The potential of the daily minimum air temperature (Tn) downscaled from general circulation models (GCMs) to predict ground-scale relative humidity in climate change studies is presented. Daily data over 50 years for Sendai city were obtained from the Japan Meteorological Agency. For GCM data, the INMCM4 model developed by the Institute for Numerical Mathematics, Russia, was selected. Statistically downscaled Tn was used with the linear or second order polynomial relationships developed for each calendar month using observed relative humidity and Tn. This produced predicted relative humidity using the alternative method. Furthermore, relative humidity was directly downscaled using the relative humidity results from the GCM. The results from the two methods were compared with the observed relative humidity data based on the root mean square error and the Kolmogorov–Smirnov test. Results based on root mean square error indicate that the predictions made using the downscaled Tn were less satisfied only for June and September compared to the results from the direct downscaling method. Moreover, the predicted relative humidity using the downscaled Tn matched rather well with the observations for 8 months (except for March, July, August and October) with calculated p values above the 0.05 level. Notably, the proposed method achieved better performance in the winter months when the directly downscaled method failed to satisfy the Kolmogorov–Smirnov test.

Decadal temperature predictions over the continental United States: Analysis and Enhancement

Increases in global temperature over recent decades and the projected acceleration in warming trends over the 21 century have resulted in a strong need to obtain information about future temperature conditions. Hence, skillful decadal temperature predictions (DTPs) can have profound societal and economic benefits through informed planning and response. However, skillful and actionable DTPs are extremely challenging to achieve. Even though general circulation models (GCMs) provide decadal predictions of different climate variables, the direct use of GCM data for regional-scale impacts assessment is not encouraged because of the limited skill they possibly exhibit and their coarse spatial resolution. Here, we focus on 14 GCMs and evaluate seasonally and regionally averaged skills in DTPs over the continental United States. Moreover, we address the limitations in skill and spatial resolution in the GCM outputs using two data-driven approaches: (1) quantile-based bias correction and (2) linear regression-based statistical downscaling. For both the approaches, statistical parameters/relationships, established over the calibration period (1961–1990) are applied to retrospective and near future decadal predictions by GCMs to obtain DTPs at ‘4 km’ resolution. Predictions are assessed using different evaluation metrics, long-term statistical properties, and uncertainty in terms of the range of predictions. Both the approaches adopted here show improvements with respect to the raw GCM data, particularly in terms of the long-term statistical properties and uncertainty, irrespective of lead time. The outcome of the study is monthly DTPs from 14 GCMs with a spatial resolution of 4 km, which can be used as a key input for impacts assessments.

Precipitation change assessment over upper Bagmati river basin using regional bias corrected GCM data

Climate change impacts on precipitation pattern have been assessed over upper Bagmati river basin, Nepal. General circulation model (GCM) projections, which are widely used to assess climate change impacts, consist of significant biases due to oversimplification of the global climate system. Bias in daily GCM precipitation was corrected at grid cell scale using regional quantile-based bias correction technique. In this technique, bias correction pattern is established by comparing GCM grid data with nearby observation station data. Concepts of homogeneous precipitation regions were used to transfer statistical characteristics of nearby observation stations to ungauged cells. Calibration and validation of the regional bias correction technique was performed over 1979-2003. Comparative precipitation change assessments were carried out at annual, monthly and daily time scales considering bias corrected precipitation data of 1979-2003 and 2075-2099 periods respectively.

Precipitation change assessment over upper Bagmati river basin using regional bias corrected GCM data

Climate change impacts on precipitation pattern have been assessed over upper Bagmati river basin, Nepal. General circulation model (GCM) projections, which are widely used to assess climate change impacts, consist of significant biases due to oversimplification of the global climate system. Bias in daily GCM precipitation was corrected at grid cell scale using regional quantile-based bias correction technique. In this technique, bias correction pattern is established by comparing GCM grid data with nearby observation station data. Concepts of homogeneous precipitation regions were used to transfer statistical characteristics of nearby observation stations to ungauged cells. Calibration and validation of the regional bias correction technique was performed over 1979-2003. Comparative precipitation change assessments were carried out at annual, monthly and daily time scales considering bias corrected precipitation data of 1979-2003 and 2075-2099 periods respectively.