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Quantifying and separating different sources of uncertainty helps to improve the understanding of the projected effects of climate change and can inform decision‐making in adaptation planning. This paper (1) evaluated four process‐based crop models; (2) assessed the effects of climate change on maize yield using climate change outputs from seven global climate models (GCMs) under three representative concentration pathways (RCPs); and (3) disaggregated the contributions of multiple crop models, GCMs and RCPs to overall uncertainty. All four models captured more than 80% of the variation in days to silking, maturity and yield, indicating reasonably reproduced observations. Similarly, the root mean square errors were moderate for days to silking and maturity (fewer than 4 days) and yield (0.5–0.7 t/ha). Overall, the results indicate that the models could assess grain yield at the study sites reasonably well. The results of the multiple models ensemble indicate that the maize yield will decrease by 9–11% with a probability of 72–80% on average during the period 2010–2039 relative to the baseline (1976–2005). The uncertainty in the maize‐yield simulations might arise mostly from the GCM models, followed by the crop models and RCPs, the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. The uncertainty in maize‐yield simulations might arise mostly from global climate models (GCMs), followed by crop models and representative concentration pathways (RCPs), the contribution of which could be neglected relative to the other factors. Therefore, the use of a multiple crop model and a GCM ensemble is advisable in order to account properly for uncertainties in crop assessments. [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]

Empirical scaling approaches for constructing rainfall scenarios from general circulation model (GCM) simulations are commonly used in water resources climate change impact assessments. However, these approaches have a number of limitations, not the least of which is that they cannot account for changes in variability or persistence at annual and longer time scales. Bias correction of GCM rainfall projections offers an attractive alternative to scaling methods as it has similar advantages to scaling in that it is computationally simple, can consider multiple GCM outputs, and can be easily applied to different regions or climatic regimes. In addition, it also allows for interannual variability to evolve according to the GCM simulations, which provides additional scenarios for risk assessments. This paper compares two scaling and four bias correction approaches for estimating changes in future rainfall over Australia and for a case study for water supply from the Warragamba catchment, located near Sydney, Australia. A validation of the various rainfall estimation procedures is conducted on the basis of the latter half of the observational rainfall record. It was found that the method leading to the lowest prediction errors varies depending on the rainfall statistic of interest. The flexibility of bias correction approaches in matching rainfall parameters at different frequencies is demonstrated. The results also indicate that for Australia, the scaling approaches lead to smaller estimates of uncertainty associated with changes to interannual variability for the period 2070-2099 compared to the bias correction approaches. These changes are also highlighted using the case study for the Warragamba Dam catchment. [ABSTRACT FROM AUTHOR]

Statistical downscaling techniques have been developed to address the spatial scale disparity between the horizontal computational grids of general circulation models (GCMs), typically 300–500 km, and point-scale meteorological observations. This has been driven, predominantly, by the need to determine how enhanced greenhouse projections of future climate may impact at regional and local scales. As point-scale precipitation is a common input to hydrological models, there is a need for techniques that reproduce the characteristics of multi-site, daily gauge precipitation. This paper investigates the ability of the extended nonhomogeneous hidden Markov model (extended-NHMM) to reproduce observed interannual and interdecadal precipitation variability when driven by observed and modelled atmospheric fields. Previous studies have shown that the extended-NHMM can successfully reproduce the at-site and intersite statistics of daily gauge precipitation, such as the frequency characteristics of wet days, dry- and wet-spell length distributions, amount distributions, and intersite correlations in occurrence and amounts. Here, the extended-NHMM, as fitted to 1978–92 observed ‘winter’ (May–October) daily precipitation and atmospheric data for 30 rain gauge sites in southwest Western Australia, is driven by atmospheric predictor sets extracted from National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis data for 1958–98 and an atmospheric GCM hindcast run forced by observed 1955–91 sea-surface temperatures (SSTs). Downscaling from the reanalysis-derived predictors reproduces the 1958–98 interannual and interdecadal variability of winter precipitation. Downscaling from the SST-forced GCM hindcast only reproduces the precipitation probabilities of the recent 1978–91 period, with poor performance for earlier periods attributed to inadequacies in the forcing SST data. Copyright © 2004 John Wiley & Sons, Ltd. [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]

Representing the West African Monsoon is a major challenge in climate modeling because of complex mechanisms of interaction across scales. We study the monsoon precipitation response to a 4K increase in sea surface temperatures using an idealized meridional‐vertical (2D) model, which allows separating regional‐ and larger‐scale influences. The 2D simulations reproduce key features of Atmospheric Model Intercomparison Project simulations and indicate that, in response to sea surface temperature increase, large‐scale changes induce a decrease of Sahel precipitation, while regional‐scale mechanisms generate a southward shift of the rainband. Large‐scale changes moisten and warm the free troposphere, while regional circulation changes increase low‐level moisture. Precipitation in the control simulations accounts for much of the variance in precipitation change for both Atmospheric Model Intercomparison Project and 2D simulations. This response is strongly connected to the large‐scale drivers of temperature and moisture, with an additional spread associated with the formulation of the convective parameterization. Plain Language Summary: The Sahel region of West Africa receives most of its annual rainfall during the monsoon season. Projections of monsoon precipitation are important to the region's growing population, yet climate models disagree on the magnitude and direction of precipitation change in future climates. This study uses a simplified model of the West African monsoon, which we show behaves similarly to complex global climate simulations. Our model allows us to disentangle monsoon changes due to directly changing sea surface temperatures from changes due to the larger‐scale response to increased temperatures. We find that both play important roles in precipitation change, the direct forcing shifts the precipitation, while the large‐scale forcing produces drying. Processes that occur on small spatial scales play an important role in producing precipitation. We investigate how a few of these are represented in our model and discover that the change in precipitation is mostly dependent on how the large‐scale forcing produces precipitation in the current climate, with a spread that is connected to how these physical processes are represented. Our study shows the usefulness of a simplified monsoon model and increases understanding of how increasing sea surface temperatures alter the West African monsoon. Key Points: A 2D monsoon model produces similar changes to general circulation models in monsoon features when sea surface temperatures are increasedSouthward shift of the rainband is connected to regional forcing, while Sahel precipitation decrease is mainly driven by larger‐scale changePrecipitation change is strongly linked to large‐scale drivers of temperature and moisture with additional spread from parameterizations [ABSTRACT FROM AUTHOR]

Despite the advances in climate change modeling, extreme events pose a challenge to develop approaches that are relevant for urban stormwater infrastructure designs and best management practices. The study first investigates the statistical methods applied to the land‐based daily precipitation series acquired from the Global Historical Climatology Network‐Daily (GHCN‐D). Additional analysis was carried out on the simulated Multivariate Adaptive Constructed Analogs (MACA)‐based downscaled daily extreme precipitation of 15 General Circulation Models and Weather Research and Forecasting‐based hourly extreme precipitation of North American Regional Reanalysis to discern the return period of 24‐hr and 48‐hr events. We infer that the GHCN‐D and MACA‐based precipitation reveals increasing trends in annual and seasonal extreme daily precipitation. Both BCC‐CSM1‐1‐m and GFDL‐ESM2M models revealed that the magnitude and frequency of extreme precipitation events are projected to increase between 2016 and 2099. We conclude that the future scenarios show an increase in magnitudes of extreme precipitation up to three times across southeastern Virginia resulting in increased discharge rates at selected gauge locations. The depth‐duration‐frequency curve predicted an increase of 2–3 times in 24‐ and 48‐h precipitation intensity, higher peaks, and indicated an increase of up to 50% in flood magnitude in future scenarios. Research Impact Statement: Extreme events pose a challenge to Coastal Southeast Virginia. A 2–3 times increase in 24‐ and 48‐h precipitation intensity resulting in up to 50% increase in flood flows is likely in the future. [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]

A cloud feedback diagnostic package is implemented in the Australian Community Climate and Earth‐System Simulator General Circulation Model, based on the methodology of "cloud radiative kernels." Using separate increased sea surface temperature and CO2 experiments, both the "rapid response" cloud contribution to forcing and temperature‐mediated cloud feedbacks are analyzed. Under increased temperature and CO2 changes, temperature‐mediated cloud radiative feedback dominates over the rapid response in the final radiative response. Cloud feedback is positive in both long and short wave, with short wave dominating global values. Contributing most to this are low to mid‐level clouds, of medium‐to‐high optical thickness. As a means of illustration of the methodology, a number of key parameters related to clouds, precipitation, and convection that are typically used in "tuning" in the model are modified. These changes result in substantial impacts on the model's current climate, but only modest changes to rapid response and feedbacks occur globally, regionally, and as a function of cloud optical thickness and height. This limited set of experiments shows that cloud adjustments and feedbacks in this model are robust under these changes, lending confidence that both model climate change projections and the conclusions of attribution studies are not overly sensitive to such parameterization tuning. Of course, a considerably larger set of experiments would be needed to demonstrate that feedbacks and rapid response are robust under the wider set of tuning adjustments commonly undertaken. Plain Language Summary: A method using a combination of sea surface temperature and CO2 forced experiments and "cloud radiative kernels" is used to investigate cloud feedbacks and "rapid responses" in the Australian Community Climate and Earth‐System Simulator climate model. The methodology is demonstrated using a small set of changes to cloud parameters that are modified in a modest way as typically "tuned" in the model. These changes substantially alter the model climate but produce only small changes to cloud rapid response and feedbacks. Cloud feedback is positive in both long and short wave, with short wave responses dominating global values, predominantly from low level to midlevel clouds. Rapid responses are weaker in both long and short wave. Key Points: The approach uses "cloud radiative kernels" to investigate cloud feedbacks and "rapid responses" in the ACCESS climate modelA small set of cloud parameters are modified in a modest way as typically "tuned" in the modelThese changes significantly alter the model climate but produce only small changes to cloud rapid response and feedbacks [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]

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]

Terminal lakes are impacted by regional changes in climate. Devils Lake ( DL), North Dakota, United States (U.S.), is a case in which a prolonged shift in the precipitation pattern resulted in a 10-m water-level rise over the past two decades, which cost over one billion U.S. dollars in mitigation. Currently, DL is 1.5 m from an uncontrolled overspill to the nearby Sheyenne River, which could lead to unprecedented environmental, social, and economic costs. Water outlets recently implemented in the lake to slow the water-level rise and prevent an uncontrolled overspill are subject to significant concerns over the introduction of invasive species and downstream water quality. We developed a hydrological model of the DL basin using the soil and water assessment tool and analyzed DL's overspill probability using an ensemble of statistically downscaled General Circulation Model ( GCM) projections of the future climate. The results indicate a significant likelihood (7.3-20.0%) of overspill in the next few decades in the absence of outlets; some members of the GCM integration ensemble suggest an exceedance probability of over 85.0 and 95.0% for the 2020s and 2050s, respectively. Full-capacity outlets radically reduce the probability of DL overspill and are able to partially mitigate the problem by decreasing the average lake level by approximately 1.9 and 1.5 m in the 2020s and 2050s, respectively. [ABSTRACT FROM AUTHOR]

The hydrologic response to statistically downscaled general circulation model simulations of daily surface climate and land cover through 2099 was assessed for the Apalachicola-Chattahoochee-Flint River Basin located in the southeastern United States. Projections of climate, urbanization, vegetation, and surface-depression storage capacity were used as inputs to the Precipitation-Runoff Modeling System to simulate projected impacts on hydrologic response. Surface runoff substantially increased when land cover change was applied. However, once the surface depression storage was added to mitigate the land cover change and increases of surface runoff (due to urbanization), the groundwater flow component then increased. For hydrologic studies that include projections of land cover change (urbanization in particular), any analysis of runoff beyond the change in total runoff should include effects of stormwater management practices as these features affect flow timing and magnitude and may be useful in mitigating land cover change impacts on streamflow. Potential changes in water availability and how biota may respond to changes in flow regime in response to climate and land cover change may prove challenging for managers attempting to balance the needs of future development and the environment. However, these models are still useful for assessing the relative impacts of climate and land cover change and for evaluating tradeoffs when managing to mitigate different stressors. [ABSTRACT FROM AUTHOR]

The southern interior ecoprovince ( SIE) of British Columbia, Canada represents the northernmost extent of the great western North American deserts, it is experiencing some of the nation's fastest economic and population growth making it one of Canada's most water-stressed regions, and it includes two headwater basins of the transboundary (Canada- US) Columbia River. Statistical trend analyses were performed on 90-year regional indicator time series for annual conditions in observed temperature, precipitation, and streamflow reflecting the three major SIE river basins: the Thompson, and transboundary Okanagan and Similkameen. Results suggest that regional climate has grown warmer and wetter, but with little net impact on total water supply availability. The outcome might reflect mutual cancellation of increases in precipitation inputs vs. evapotranspiration losses. Conclusions appeared largely insensitive to low-pass data filtering, Pacific Decadal Oscillation effects, or solar output variability. Ensemble historical global climate model runs over the same time interval support this absence of appreciable trend in regionally integrated annual runoff volume, but a possible mismatch in precipitation results suggests a direction for further study. Overall, while important changes in hydrologic timing and extremes are likely occurring here, there is limited evidence for a net change in overall water supply availability over the last century. [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]

Hydrologic impacts of global climate change are usually assessed by downscaling large-scale climate variables, simulated by general circulation models (GCMs), to local-scale hydrometeorological variables. Conventional multisite statistical downscaling techniques often fail to capture spatial dependence of rainfall amounts as well as hydrometeorological extremes. To overcome these limitations, a downscaling algorithm is proposed, which first simulates the rainfall state of an entire study area/river basin, from large-scale climate variables, with classification and regression trees, and then projects multisite rainfall amounts using a nonparametric kernel regression estimator, conditioned on the estimated rainfall state. The concept of a common rainfall state for the entire study area, using it as an input for projections of rainfall amount, is found to be advantageous in capturing the cross correlation between rainfalls at different downscaling locations. Temporal variability and extremities of rainfall are captured in downscaling with multivariate kernel regression. The proposed model is applied for downscaling daily monsoon precipitation at eight locations in the Mahanadi River basin of eastern India. The model performance is compared, with a recently developed conditional random field based as well as with established multisite downscaling models, and is found to be superior. Analysis of future rainfall scenarios, projected with the developed downscaling model, reveals considerable changes in rainfall intensity and dry and wet spell lengths, among other things, at different locations. An increasing trend of rainfall is projected for the lower (southern) Mahanadi River basin, and a decreasing trend is observed in the upper (northern) Mahanadi River basin. [ABSTRACT FROM AUTHOR]

Global climate models are constantly being upgraded, but it is often not clear what these changes have on climate change impact projections. We used difference maps to directly compare downscaled projections of temperature and precipitation across North America for two versions (or generations) of three different Atmospheric-Ocean General Circulation Models (AOGCM)s. We found that AOGCM versions differed in their projections for the end of the current century by up to 4 °C for annual mean temperature and 60% for annual precipitation. To place these changes in an ecological context, we reanalyzed our work on shifts in tree climate envelopes (CEs) using the newer-generation AOGCM projections. Based on the updated AOGCMs, by the 2071-2100 period, tree CEs shifted up to 2.4 degrees further north or 2.6 degrees further south (depending on the AOGCM) and were about 10% larger in size. Despite considerable differences between versions of a given AOGCM, projections made by the newer version of each AOGCM were in general agreement, suggesting convergence across the three models studied here. Assessing the AOGCM outputs in this way provides insight into the magnitude and importance of change associated with AOGCM upgrades as they continue to evolve through time. [ABSTRACT FROM AUTHOR]