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ABSTRACT The biases in the Global Circulation Models ( GCMs) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non-stationary bias correction model, termed residual-based bagging tree ( RBT) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non-stationarities into the modelling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared with the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios ( RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44, 2.59, and 4.71 °C by the end of the century, respectively, when compared with the average temperature during 1970-1999. [ABSTRACT FROM AUTHOR]

One way in which global climate model (GCM) output can be utilized to infer local impacts is through the use of synoptic climatology: creating a set of atmospheric patterns that capture the variability in the climate system, and then analyzing trends and variability in the frequency of these patterns moving into the future. In this paper, we demonstrate a new synoptic climatological technique for classifying atmospheric patterns that can be used in conjunction with GCM output data (in this case, the Community Climate System Model 3). We apply this method to 850-hPa temperature patterns over the contiguous United States to derive daily categorizations. A total of 15 clusters are created from the data set; once the mean GCM bias is removed, historical cluster frequencies in the GCM data set are not statistically different from those of the reanalysis data set. In the future, significant changes in frequency are observed across most of the transition season clusters, as they broaden in seasonality at the expense of winter clusters, some of which nearly entirely disappear. Changes are greater moving further into the future, and greater for the more carbon-intensive special report on emissions scenarios (SRES) scenarios (A1FI, A2) than the less-intensive scenario tested (B1). Diagnostics test how well the mean patterns of the reanalysis data set, GCM historical data set and the future GCM data sets resemble each other. For some clusters, mean bias between the historical and future data sets grows substantially by the end of the 21st century under the more carbon-intensive scenarios. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The present research focuses on temperature change signals over the Carpathian Basin with a special focus on selected lowland and mountainous subregions. High‐resolution (0.11°) EURO‐ and Med‐CORDEX regional climate model (RCM) simulations of near‐surface air temperature are analysed based on raw and bias‐adjusted data. The mini‐ensemble consists of eight RCM simulations driven by five different general circulation models for the period 1976–2099 under the high‐end RCP8.5 scenario. The high‐resolution, homogenized and quality controlled CARPATCLIM was used as a reference dataset. The selected subregions cover eight municipalities located at diverse altitudes: Bratislava, Budapest, Brassov, Debrecen, Hoverla, Novi Sad, Pécs and Poprad. The following climate indices are assessed: summer days, ice days, frost days, tropical nights, the coldest day, the warmest day, the coldest night and the warmest night. In general, for the reference period (1976–2005) bias‐adjusted RCM data showed almost perfect match with observations. Accordingly, no best performing RCM is found for all indices. The ensemble mean of the bias‐adjusted RCM simulations projects an increase (decrease) of 32% and 112% (18% and 25%) in the annual number of summer days and tropical nights (frost days and ice days) for the period 2021–2050. For 2070–2099 we can expect more frequent tropical nights (about five times) with respect to the reference period and the frequency of frost days can be halved. Profound warming manifests in the increase of the warmest temperature of day of up to 2–3°C by the near future and of 5–7°C by the end of the 21st century, which means the absolute maximum temperature can reach 44–47°C for the period 2070–2099. Our results also highlight the need for bias‐adjusted data adapted by different sectors (human health, agriculture, transport, disaster management, heritage conservation) under the national adaptation strategies. [ABSTRACT FROM AUTHOR]

El Niño–Southern Oscillation (ENSO) is one of the most important modes of climate variability on interannual timescales. We aim to find out whether a change in ENSO frequency can be predicted for the nearer future. We analyse the unforced pre‐industrial control run and the forced 1%/year CO2 increase run for an ensemble of 43 general circulation models that participated in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We assume that the uncertainty of ENSO frequency trend estimates from an ensemble is caused by apparent trends as well as model differences. The part of the uncertainty caused by apparent trends is estimated from the pre‐industrial control simulations. As a measure for ENSO frequency, we use the number of El Niño‐ and La Niña‐like months in a moving 30‐year time window. Its linear decadal trend is calculated for every member. The multimember mean of the trend for both experiments is less than 0.7 events per decade. Given that the standard error is of the same order of magnitude, we consider this a negligible trend. The uncertainties are large in both experiments and we can attribute most of the intermember variability to apparent trends due to natural variability rather than different model reactions to CO2 forcing. This means that the impact of intermodel differences might have been overstated in previous studies. Apparent trends make it very difficult to make reliable predictions of changes in ENSO frequency based on 120‐year time series. [ABSTRACT FROM AUTHOR]

The Northeast Brazil (NEB) is considered a region that is strongly vulnerable to extreme rainfall events due to climate change. In this context, this study evaluates the performance of 12 general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in representing the number of dry (DD) and heavy rainfall days (HRD) over NEB in the historical period (1981–2005) and their projections for the near (2016–2040) and far future (2076–2100) under three Shared Socioeconomic Pathways (SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5) scenarios. For selection of extreme rainfall days, we used the absolute threshold of less than 1 mm for DD and the 99th percentile for HRD. The results indicate that four (three) models show an overall superior performance in reproducing the dry (heavy rainfall) days, being common in both aspects only the EC‐EARTH3. Thus, the skill of the CMIP6 models for NEB varies according to the extreme rainfall conditions analysed. For the future climate (near and far), results show that dry days are project to increase over the entire NEB territory, especially during DJF and MAM and more pronounce in the east coast, with projections that these conditions will be more severe under the SSP5‐8.5 scenario. The number of dry days may increase up to 15%. For HRD, although the results indicate that the number of days with heavy precipitation will be more frequent in the future (the increase can exceed 140%), the analysis show that under the low (SSP2‐4.5) or intermediate (SSP3‐7.0) forcing scenarios the HRD tends to be higher than in the most pessimistic scenario (SSP5‐8.5). Such result was explained according to the dryness of the atmosphere. Therefore, this study shows that it might either rain too much within a short range of time or the water scarcity will be longer‐lasting in the future in NEB. [ABSTRACT FROM AUTHOR]

Understanding of future changes in drought characteristics is crucial for climate change adaptation and drought impact mitigation. We analysed the projected changes in drought characteristics relative to historical drought conditions in Central Asia using the Standardized Precipitation Evapotranspiration Index based on both gridded observations and an ensemble of bias‐corrected and spatially disaggregated global circulation models (GCMs) from phase 6 of Coupled Model Intercomparison Project (CMIP6). The results suggest that precipitation and potential evapotranspiration are projected to increase across Central Asia. Even though the change in wetness may not be significant and robust, a "dry gets drier and wet gets wetter" pattern may emerge in future under different scenarios. Drought events in Central Asia's semi‐arid and arid regions (aridity index <0.5) are projected to become more frequent (>125%), with longer duration (>55%), higher severity (>74%) and intensity (>8%) by the end of the 21st century under four Shared Socioeconomic Pathway (SSP) scenarios. In the coming period of 2021–2050, Central Asia is expected to have more drought events with a longer duration but lower intensity. Comparisons between different SSP scenarios stress the importance of climate change mitigation strategies to avoid more drought occurrence, longer drought duration and higher drought severity. The long‐term mitigation and adaptation studies for increasing drought impacts are also imperative in Central Asia. [ABSTRACT FROM AUTHOR]

Maize production on the North China Plain (NCP) is critical to food security in China; however, currently, it is affected by climate change. Understanding the spatiotemporal distribution of the climate suitability for maize on the NCP in the present and future may help sustainably use climate‐related resources to ensure food security in China. In this study, 30 general circulation models from the Coupled Model Intercomparison Project Phase 5 and a statistical downscaling model (NWAI‐WG) were used to project meteorological data in 2021–2100 under representative concentration pathway 4.5 (RCP4.5) and RCP8.5 for 23 national climatic stations on the NCP. Based on agricultural climate suitability theory and the fuzzy mathematics method, the suitability of temperature, precipitation, and solar radiation on summer maize were analysed. The results showed that temperature suitability is decreasing for 2021–2100, especially during the stages from jointing to maturity; temperature suitability is the lowest in the southwest and increases to the northeast under both scenarios. Compared to 1996–2015, the precipitation suitability in 2021–2100 increases greatly under both scenarios, especially in the central part under RCP4.5 and in the north part under RCP8.5. Solar radiation suitability shows a decreasing trend for 1996–2015, however, an increasing trend for 2021–2100 under both scenarios. At spatial scale, the solar suitability increases from southwest to northeast. The integrated climate suitability under RCP4.5 in 2021–2100 is averaged approximately 0.8 and varies slightly, indicating climate change may do small effect on maize growth, though the high values shifting from the central part in 2021–2040 to the northern part in 2081–2100; however, under RCP8.5, the integrated climate suitability shows a downward trend, indicating that climate change will make many regions less suitable for maize growth. These results could provide basic information for agriculture to adapt to climate change and ensure food security for China. [ABSTRACT FROM AUTHOR]

Future changes in climate variable exhibit prominent impact on flood magnitudes, crop yields, and freshwater withdrawal. Researchers typically ignore the large degree of uncertainty translated from climate projections to the estimated climate change magnitudes while applying pre‐processing approaches on climate change projections. General Circulation Models (GCM) exhibit substantial uncertainty in projecting future changes in the seasonal temperature, which is evaluated by estimating the shift in either the mean or variance. Bias between the observed changes (1950–1999) and GCM simulated changes vary across models, climate regions, seasons, and under emission scenarios. The simplest approach to reduce model structural uncertainty, equal weighting of GCMs, does not consider superiority of one or multiple GCMs compared to the rest. The current study adopts a performance‐based model combination approach that has shown efficiency in streamflow and weather forecasting, and GCM precipitation simulation. The optimal model combination approach has been modified to combine multi‐model climate change information, while yielding the spatial correlation in climate change information within a geographic region. The optimal model combination approach, along with a simple bias‐correction, is applied on 10 GCMs over nine climate regions across the coterminous United States (CONUS). We found that the optimal combination exhibits lower RMSE values as compared to the equal combination. Correlations between the model combined projections under optimal combination and the observed changes are strong and positive (>0.5). Future (2000–49) model combined projections exhibit an increase in the mean seasonal temperature by 2°C for winter and by 1°C for summer over almost all climate regions. [ABSTRACT FROM AUTHOR]

Socioeconomic development and natural ecosystems of China are strongly affected by droughts. The socioeconomic risk of droughts in China should be quantified under climate change. This study assessed drought impacts on population and gross domestic product (GDP) under the 2.0°C‐warmer climate scenario, by implementing multiple general circulation model (GCM) simulations under the representative concentration pathway (RCP). Using the standardized precipitation evapotranspiration index (SPEI), we calculated drought frequencies in the base period (1986–2005) and 2.0°C‐warmer climate (2040–2059 in RCP4.5). Then, population and GDP exposures were evaluated by combining the drought frequency with population and GDP simulations under a shared socioeconomic pathway (SSP). Droughts were likely to occur more frequently under the 2°C‐warmer climate, especially for inland areas of northern China (i.e., Xinjiang, Xizang, Qinghai, Ningxia, Shaanxi, Gansu provinces), consisting mainly of mild and moderate droughts. Population exposure to drought was projected at 4.94 ± 0.36 × 109 person‐months, around 2% increase over the base period value. GDP exposure would increase significantly to 2.08 ± 0.15 × 1014 PPP $‐months, approximately 14‐fold over the base period. Probability analysis showed nearly 50% of the grid cells of China's entire territory undergoing more than an 11‐fold increase in GDP exposure. Our findings indicated the climate and GDP effects were primary contributors, respectively, to the changed population and GDP exposures to drought. Population and GDP exposures were distributed unequally across the country, having a large impact in the east where both population and economic activity are highly agglomerated. Our results reveal an urgent need to design and implement effective adaptation measures to overcome droughts, especially in eastern China. [ABSTRACT FROM AUTHOR]

Despite intense research on climate change (CC), regional studies for Central America, which is considered a CC hot spot, remain scarce. The information provided by general circulation models (GCMs) is too coarse to accurately reproduce local‐scale climatic features, which are needed for impact assessment. Thus, downscaling techniques are employed to address this scale mismatch. Costa Rica is the present case study, for which suitable predictors were tailored for downscaling related to regional climatic characteristics, such as the Inter‐Tropical Convergence Zone, El Niño Southern Oscillation, the Caribbean Low‐Level Jet, and the Mid‐Summer Drought. Statistical downscaling models were calibrated for precipitation, maximum and minimum temperature, using the perfect prognosis methodology by means of station data, ERA‐INTERIM reanalysis and artificial neural networks, yielding satisfactory results. As found in several studies, the temperature models replicated more accurately the statistics of the observed datasets. However, here, through the implemented approach and the tailored predictors, the precipitation models conveyed an improvement compared to standard methods. Projected daily climate was obtained employing CORDEX data under the RCP8.5 scenario for the central region of the country. Overall, the changes in climate estimated by the end of the 21st century agree with coarser‐scale projections. Finally, projected climate extremes indices were calculated and rendered further details on the intensity of future CC by the end of the century. [ABSTRACT FROM AUTHOR]

The selection of general circulation models (GCMs) with high capability to represent the past and likely future climate for a specific geographical location is a crucial step to assess impacts of climate change on different sectors. This study included pool of 105 and 78 GCMs for representative concentration pathways (RCPs) 4.5 and 8.5, respectively, from Coupled Model Intercomparison Project—Phase 5, applied an advanced envelope‐based selection approach to select representative GCMs for the Koshi river basin in China and Nepal at short‐term (2016–2045), mid‐century (2036–2065) and end‐of‐century (2071–2100) periods, and developed range of possible future precipitation and temperature scenarios with high resolution downscaled data (10 × 10 km2), which is the novelty contribution of the study. Considering RCP4.5, average annual precipitation is expected to increase by 0–16%, 4–23% and 4–24% in the short‐term, mid‐century and end‐of‐century periods, respectively. Using RCP8.5, equivalent predictions are 6–20%, 6–36% and 13–49% in the short‐term, mid‐century and end‐of‐century periods, respectively. Average annual temperature is expected to increase, but with higher increases during winter than in the monsoon period. Considering RCP4.5, average annual temperature is expected to increase by 1–1.4°C, 1.3–1.9°C and 1.6–2.8°C in the short‐term, mid‐century and end‐of‐century periods, respectively. Similarly, using RCP8.5, equivalent predictions are 1–1.6°C, 1.8–2.9°C and 3.1–5.6°C in the short‐term, mid‐century and end‐of‐century periods, respectively. The ensemble mean of absolute change in average precipitation and temperature projects that High Himalaya and Tibet regions are more sensitive to climate change considering precipitation and temperature, respectively. The results also suggest that GCMs selection for a catchment varies with climate scenarios and specific future time periods. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0), which is an indicator of atmospheric evaporation demand over a hypothetical reference surface, is expected to alter along with global climate change. In this study, the changes and driving forces of historical ET0 and its future projections in Xinjiang, China, were comprehensively conducted. The trend analysis revealed that regional ET0 decreased significantly at a rate of −4.1 mm/year2 during 1961–1993 and increased at a rate of 3.4 mm/year2 during 1994–2010. To explore the possible causes, the contributions of major climatic variables to the ET0 trends were derived based on the differential equation method. During 1961–1993, the decreasing trend of ET0 was primarily ascribed to the wind speed. The integrated negative effect from the decrease in wind speed (U) and increase in relative humidity (RH) was more significant than the positive effect from the increase in air temperature (T), resulting in the decrease in ET0. During 1994–2010, combined with increasing T and decreasing RH, the increase in U offset the effect of the net radiation (Rn) decrease and led to an increase in ET0. Future ET0 projections over three periods (2010–2039, 2040–2069, and 2070–2099, which are named as 2020s, 2050s, and 2080s, respectively) were conducted based on the general circulation models under two representative concentration pathways scenarios (RCP4.5 and RCP8.5). A continuous increasing trend in ET0 was demonstrated in the 21st century. The increase in ET0 will increase the irrigated water resource consumption and bring new challenge to water resource management in this area. [ABSTRACT FROM AUTHOR]

Climate change is known as one of the key challenges of the 21st century for sustainable agricultural development over Southwest Asia. However, not much is known about the way that climate changes might affect precipitation, temperature, and climate zones as their consequence over Southwest Asia. Here we have analysed probable changes in modified Thornthwaite climate zones by using monthly temperature and precipitation data from 320 meteorological stations during the historical period of 1971–2015, as well as the future projections of 17 GCMs (General Circulation Models) outputs under the RCP4.5 and RCP8.5 scenarios in two future periods, 2041–2070 and 2071–2100. The CMIP5 climate projections were bias corrected using the correction factors derived in the historical period and applied to simulate future climatic conditions over Southwest Asia. Results derived from multi model ensemble projections of precipitation and temperature indicated that the regional mean annual temperature by the future period of 2071–2100 is expected to increase by 2.4 and 3.8°C, under RCP4.5 and RCP8.5, respectively. Also, except for the southern and eastern parts of the Arabian Peninsula, by 2071–2100, the mean annual precipitation would significantly decrease by on average 6.2 and 7.5%, under RCP4.5 and RCP8.5, respectively. Computing climate types based on averages from three climatic periods (1971–2015, 2041–2070, and 2071–2100) showed that Southwest Asia tends to become warmer and dryer. For example, by the end of the 21st century, the percentage of the area occupied by the torrid‐arid climate type (which already covers 13.8% of the total area) would be 29.3 and 36.4% under RCP4.5 and RCP8.5, respectively. These changes are important because they would definitely have profound ecological, hydrologic, and social consequences and may affect the basic components of sustainable agricultural systems like crop and livestock productions. [ABSTRACT FROM AUTHOR]

Future changes in wave climate will influence the marine ecosystem, coastal erosion, design of coastal defences, operation of near‐ and off‐shore structures, and coastal zone management policies and may further add to the potential vulnerabilities of coastal regions to projected sea level rise. Many studies have reported changes in the global wave characteristics under climate change scenarios, but it is important to project future changes in local/regional wave climate for smooth implementation of policies and preventing severe coastal erosion and flooding. In this study the regional wave climate along the Indian coast for two time slices, 2011–2040 and 2041–2070, is reported using an ensemble of near‐surface winds generated by four different CMIP5 general circulation models (GCMs), under RCP4.5 scenario. Comparison of the wave climate for the two time slices shows an increase in wave heights and periods along much of the Indian coast, with the maximum wave heights increasing by more than 30% in some locations. An important finding is that at most locations along the east coast, wave periods are expected to increase by almost 20%, whereas along the west coast an increase of around 10% is expected. This will alter the distribution of wave energy at the shoreline through changes in wave refraction and diffraction, with potential implications for the performance and design of coastal structures and swash‐aligned beaches. Furthermore, the computations show material changes in the directional distribution of waves. This is particularly important in determining the longshore transport of sediments and can lead to realignment of drift‐aligned beaches, manifesting itself as erosion and/or siltation problems. This study is a preliminary contribution towards regional climate projections for the Indian Ocean region which are needed to plan and mitigate the impacts of future climate change. [ABSTRACT FROM AUTHOR]

The arid and semi‐arid areas in Central Asia have scarce water resources and fragile ecosystems that are especially sensitive and vulnerable to climate change. Reliable information regarding future projections of change in climate is crucial for long‐term planning of water resources management and structural adjustment of agriculture in this region. However, the low‐density meteorological observation network brings great challenges for investigating the effects of climate variations. In this study, variations of precipitation and temperature in Central Asia were examined by a combination of gridded climate dataset of the Climate Research Unit and the latest general circulation models (GCMs) under a representative concentration pathway 4.5. Three downscaling methods, Delta, Advanced Delta, and Bayesian model averaging (BMA) methods, translate the coarse GCMs to local climatic variations for the period 2021–2060 relative to 1965–2004. Major results suggested that the Advanced Delta and BMA methods outperformed the Delta method in precipitation downscaling. Projected precipitation exhibited a general increasing trend at a rate of 4.63 mm/decade for entire Central Asia with strong spatiotemporal heterogeneity. While a declining trend was observed in the southwestern and central parts of Central Asia in summer. The projected temperature was revealed an obvious ascending at 0.37°C/decade, while the warming rate accelerated in higher latitude and mountainous areas. [Correction added on 03 December 2018, after first online publication: The preceding statement has been corrected in this version.] The surface land coverage had significant effects on the variations of precipitation and temperature, respectively. The driven factors of local climate were suggested by analysing the relationships between climate variations and large‐scale atmospheric circulation fields anomalies. The findings of this study aims to provide useful information to improve our understanding for future climate change and benefit local decision makers. The performance of three downscaling methods in terms of precipitation and temperature from general circulation model (GCM) outputs were compared in this study. The relationships between climatic variations and different land cover types were detected based on Land‐Use and Land‐Cover Change (LUCC) and NDVI datasets. The possible drivers of local climatic variations were also suggested by analysing the relationships between local climatic variations and large‐scale atmospheric circulation fields. [ABSTRACT FROM AUTHOR]

Southern Africa rainfall (SAR) is generally projected to decrease during the 21st century as a result of climate change, although there is some disagreement regarding the location and magnitude of this reduction in general circulation models (GCMs). Here we examine the robustness of the rainfall response to sea surface temperature (SST) anomalies. Previous work argues that warmer SSTs in the Indian Ocean suppress SAR. Other studies argue that El Niño leads to suppressed SAR. We examine the SAR response to SST anomalies in the Indian Ocean, Atlantic Ocean, and Niño3.4 region both in observations and in two large ensembles of GCMs run over the 20th and 21st century. We find that El Niño–Southern Oscillation (ENSO) SSTs are most correlated with SAR, while correlations between SAR and the Indian Ocean are dominated by their respective responses to ENSO. This relationship appears to persist under a warming background state. CMIP5 MMM percentage change in rainfall for JFM, between 1970–2000 and 2070–2100. The black outline defines the SAR region emphasized here; the pink outline defines the region used in Funk et al. (2016) (where slightly different months were used, DJF), and the green outline defines the region used in Hoerling et al. (2006) (where FMA was used). [ABSTRACT FROM AUTHOR]

ABSTRACT: Climate change has and will continue to impact the magnitude and timing of streamflow throughout the world. While streamflow timing of western United States watersheds has shifted earlier in mountainous snow‐dominated watersheds due to earlier snowmelt, timing for rain‐dominated coastal watersheds has shifted to later in the year. Despite these dissimilarities, coastal watersheds have received little attention in the literature. This research assesses changes in projected climate and hydrology for five small coastal basins (42–718 km2) in Washington, Oregon, and California. Projections of temperature and precipitation from General Circulation Models (GCMs) for Representative Concentration Pathways 4.5 and 8.5 are coupled with the Soil and Water Assessment Tool hydrologic model to simulate future hydrology for each watershed. The response of climate, streamflow discharge, and timing was analysed for each watershed by comparing the projected mid‐21st century (2035–2064) and late‐21st century (2070–2099) to the historical period (1970–1999). While temperature is projected to increase at each watershed from 1 to 6 °C by the end of the 21st century, precipitation varies widely with the median and mean change across all watersheds and GCMs being close to zero (just 2.0 and 3.6%, respectively). Changes in the magnitude of peak winter streamflow discharge differ across the region compared to the historical period. Streamflow timing also features latitudinal divergence, with the southern California watershed projecting later timing by 13 days and all other watersheds indicating smaller shifts (0–7 days) for the end of the 21st century compared to the historical time period, largely from corresponding shifts in precipitation timing. The projected decreases in peak winter streamflow magnitude, particularly in central and southern California, have critical implications for water supply, which is already strained from extensive drought and high demand. This research makes strides to assess new trends across coastal watersheds. [ABSTRACT FROM AUTHOR]

This study analyzes temperature projections in the Koshi River Basin in Nepal using data obtained from ten General Circulation Models (GCMs) for three IPCC Special Range of Emission Scenarios (SRES): B1, A1B and A2. Low resolution data of minimum and maximum temperature obtained from the selected GCMs was downscaled using the statistical downscaling model Long Ashton Research Station Weather Generator (LARS-WG) for ten stations located in two physiographic regions of the study area: the Middle Mountains (1500-2700 m) and the Siwalik Hills (700-1500 m). The projected temperature and differences in projections among individual GCM projections for changes in the mean value of seasonal and annual Tmin and Tmax are presented for three future periods: 2011-2030 (2020s), 2046-2065 (2055s) and 2080-2099 (2090s). We also analyzed the baseline period and future Tmin and Tmax data through seven indices, as recommended by the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI). Results show that the LARS-WG model performs well when downscaling Tmin and Tmax. An increase in seasonal as well as mean annual minimum and maximum temperature is projected for all three future periods. Projected warming, as well as the differences among projections from different GCMs, increases with time for each of the three scenarios. The cold years during the 2055s and 2090s are expected to be hotter than the hot years during the baseline period. The increase in temperature, as well as the range of uncertainty, is expected to be higher in the Mountains than in the Hills. The number of summer days and tropical nights is expected to increase during all three future periods. The temperature of the coldest day, coldest night, warmest day and warmest night is also expected to increase in both the regions during all three future periods. [ABSTRACT FROM AUTHOR]

Ranking the performance of global climate models (GCMs) is a notoriously difficult exercise. Multi-model comparison studies nearly always show that each model has strengths and weaknesses relative to others, and for many purposes the multi-model ensemble mean delivers better estimates than any individual model. Nevertheless, in regions like East Africa, where there is little consensus between models on the magnitude or sign of 21st century precipitation change, the multi-model ensemble mean approach to climate projection provides little value for adaptation planning. Here, we consider several possible frameworks for model evaluation and ranking, and assess the differences in performance of a subset of models participating in the 5th Coupled Model Intercomparison Project (CMIP5) according to each framework. Our test case is precipitation in the Nile River headwaters regions. We find that there is little consistency in the relative performance of models across frameworks based on amount and seasonality of precipitation, interannual precipitation variability, precipitation teleconnections, and continental scale climate patterns. These analyses offer some guidance on which GCMs are most likely to provide meaningful results for specific applications, but they caution that any effort to select 'best performing' GCMs for the Nile River basin must carefully consider the purposes for which GCMs are being selected. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, an attempt was made to investigate a future wind-speed change over the Republic of Korea using a mesoscale weather research and forecasting ( WRF) model. We presented dynamically downscaled wind fields and the physical processes that derived the surface wind changes. A double-nested WRF model within the global-scale scenario from the fifth generation of the ECHAM general-circulation model ( ECHAM5) was integrated for the present-day (the late 20th century, 1990-1999) and future (the mid-21st century, 2045-2054) wind climate. The ensemble mean of the simulated results for the sensitivity of planetary boundary-layer physics provided a relative advantage in determining synoptic and surface fields. A reasonable and realistic wind climatology, influenced by the asymmetric heating of land and sea, was identified over the East Asian monsoon region. The observed spatial and temporal distributions of temperature and wind speed were also reproduced well by the mesoscale simulations. Projected wind-speed changes at 80 m above ground level varied from -9.53 to 29.80% depending on the location and season. [Correction added 3 March 2014 after original online publication: in the preceding sentence '9.53' has been corrected to '-9.53'.] The strong wind speed in the cold season decreased (-5.45%), whereas the weak wind speed in the warm season increased (4.54%). A possible cause of the weakened and strengthened winds in cold and warm seasons, respectively, is shown to be the effects in the lower-tropospheric pressure-gradient force accompanied by pronounced warming. [ABSTRACT FROM AUTHOR]

The resolution of general circulation models ( GCMs) is too coarse to assess the site-specific impacts of climate change. Downscaling approaches have been developed to meet this requirement. As the resolution of climate model increases, it is imperative to know whether the finer resolution of regional climate models ( RCMs) would result in any improvement of statistical downscaling quality at the station scale for particular downscaling methods. The objective of this study is to assess the effects of climate model resolutions on statistical downscaling quality of precipitation using the generator for point climate change ( GPCC) model. The downscaling is conducted across three scales, from GCM, and mid- and high-resolution RCMs to a station scale for two Canadian stations in the Quebec province. Observed precipitation gridded to the corresponding scales is also studied in parallel, totalling six downscaling experiments. The results show that the statistics of downscaled precipitation are somewhat overestimated for the Sept-Iles station for all six downscaling experiments with the relative error of mean daily, monthly, and annual precipitation ranging between 1.1 and 5.0%, between 2.7 and 5.9% and between 0.5 and 6.2%, respectively, but underestimated for the Bonnard station with the relative error ranging between −4.1 and −10.2%, between −3.2 and −13.3%, and between −2.6 and −12.0%, respectively. The number of wet days per year is well preserved with the difference between observed and downscaled data ranging between −2.9 and 6 d across all downscaling experiments and two stations. The quality of downscaled precipitation is similar between using gridded observations and model-simulated data for both stations. Furthermore, there is no noteworthy scale effect on downscaling quality when downscaling climate model output with GPCC, indicating that for regions without RCM projections, high-quality daily series at stations can be derived directly from GCM projections with this particular downscaling model. © 2013 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

ABSTRACT It is important to investigate potential changes in temperature, precipitation and solar radiation for assessing the impacts of future climate change on agricultural production for specific regions. In this study, climate scenarios of precipitation, temperature and solar radiation for the North China Plain ( NCP) were constructed in terms of stochastic daily weather sequences. A nonhomogeneous hidden Markov model ( NHMM) was used to downscale daily precipitation projections at 32 stations during winter wheat and summer maize growing seasons for a baseline (1966-2005) and a 21st century (2080-2099) A1B scenario, using selected general circulation models ( GCMs). A climatological seasonal cycle of regional-averaged daily reanalysis precipitation was used as input to the down-scaling for the baseline simulation; this input was then scaled by the precipitation changes from GCM projections to generate down-scaled stochastic simulations of precipitation in the 21st century. Temperature was generated using a weakly stationary generating process, conditional on precipitation occurrence, with 21st century additive changes taken from the GCMs at the regional scale. Three hypotheses about changes in solar radiation (−20%, 0% and 20%) were made considering the large uncertainty in its future change. The down-scaled simulations exhibit station increases in the mean daily rainfall of 13.9-69.7% in the scenarios driven by the GCM with the projected largest and multi-model mean precipitation increase for the wheat season, with changes of 0.4-29.9% for the maize season. In the scenario driven by the GCM with the largest projected precipitation decrease, the simulated rainfall decreases at all stations, with changes ranging from −24.6 to −0.1% for the wheat and maize seasons, respectively. Temperature increases by about 3.7 °C for the wheat season and 3.6 °C for the maize season. [ABSTRACT FROM AUTHOR]

This study is based on the study from Matulla et al. (2009) where the glacier under estimation has been Peyto Glacier in Canada. The same methods have been used for five Austrian glaciers; projections of glacier mass balance are generated from ensembles of general circulation model (GCM) simulations by the use of direct statistical downscaling. Thereby, the general features of the atmospheric circulation over an expanded geographical region covering the European Alps are linked empirically to winter and summer mass balance records measured at five glaciers in Austria. The projections are taken from an ensemble of ECHAM5-MPIOM simulations forced with the IPCC-SRES scenarios A1B and B1. Results based on the statistical downscaling indicate decreasing balances for both winter and summer. These results suggest continued frontal recession and downwasting of the alpine glaciers in this region until 2100. For Jamtalferner, these suggestions reach reductions of about 1000 mm water equivalent in summer. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Three formulations of a non-parametric stochastic downscaling framework for simulation of daily rainfall at multiple point locations for catchment-scale climate change impact assessment under enhanced greenhouse conditions are evaluated. In the first case, rainfall is downscaled using a system of variables that combine atmospheric circulation indicators (geo-potential height, pressure and derived variables) with time-lagged wetness indicators reflecting a continuous rainfall state. In the second and third cases, the above system of variables is augmented using indicators of atmospheric moisture found relevant in defining the downscaling relationship. The downscaling framework is specified using 43 years of daily rainfall observations at 30 locations near Sydney, Australia, with reanalysis data being used to represent observed atmospheric variables. A single ensemble member of the CSIRO Mark 3 general circulation model (GCM) (corresponding to the SRES A2 emission scenario) is used for comparison of downscaled rainfall for current and future (year 2070) conditions. While all the downscaling formulations show an overall similarity in the downscaled rainfall for the current climate, variations for some rainfall attributes are observed for year 2070 conditions. A variable convergence score (VCS) is employed to assess the relative skill of atmospheric predictors used in the different formulations. The VCS indicates use of temperature depression (difference of dew point and atmospheric temperatures) as an indicator of atmospheric moisture to obtain more realistic estimates of rainfall for year 2070. In general, a decrease in winter and spring and an increase in summer in the frequency of wet days with a decrease in the winter rainfall amount in the year 2070 is projected. Also, inland stations are found to be more sensitive to the expected future climate changes in comparison to the coastal ones. Copyright © 2009 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The article presents a study which aims to evaluate the impact of climate change on streamflow using the Soil and Water Assessment Tool (SWAT) model. It presents the analysis performed on the Nzoia catchment in the Lake Victoria basin in Kenya, and the significance of the changes in temperature and precipitation detected by the global circulation models (GCMs). It also offers insights on surface runoff, base flow, probability of flood and the world's water resources.

The article presents a study which investigates the climate-change signal in southern and tropical Africa as projected by a high-resolution numerical regional climate model (RCM). Researchers used a global circulation model (GCM) and conformal-cubic atmospheric model (CCAM) to simulate the climate change in the region from 1975-2005 and 2070-2100. Results show that simulation of the subtropical high-pressure belt in winter has enhanced zonal flow in South Africa and a displacement of frontal rain bands towards the south. The occurrence of cloud bands that constitute the South Indian Convergence Zone (SICZ) over the southeastern subcontinent is the result of simulating the Indian Ocean High (IOH).

A climate change resulting from recent increases in the atmospheric greenhouse gases (GHGs) is supposed to occur sometime in the middle of the next century. This climate change induced by GHGs would be caused by radiative forcing through the greenhouse effect and would lead to global warming and eventually to sea-level rise. This paper presents a climate change scenario for the greater Caribbean as simulated by the Canadian Climate Centre (CCC) general circulation model (GCM). The CCC GCM projects a GHG induced temperature increase of about 2 0Cand more extreme rainfall conditions for the greater Caribbean. The climate records of temperature, rainfall and cloudiness for several stations on the island of Trinidad are then examined in an attempt to detect GHG climate change signals. The temperature change to date, close to 1 0C, and the fluctuations in rainfall patterns, over the lastfive decades, seem to be indicative of early greenhouse signals. ) 1997 by the Royal Meteorological Society. [ABSTRACT FROM AUTHOR]