Search

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

This study evaluates regional-scale projections of climate indices that are relevant to climate change impacts in Canada. We consider indices of relevance to different sectors including those that describe heat conditions for different crop types, temperature threshold exceedances relevant for human beings and ecological ecosystems such as the number of days temperatures are above certain thresholds, utility relevant indices that indicate levels of energy demand for cooling or heating, and indices that represent precipitation conditions. Results are based on an ensemble of high-resolution statistically downscaled climate change projections from 24 global climate models (GCMs) under the RCP2.6, RCP4.5, and RCP8.5 emissions scenarios. The statistical downscaling approach includes a bias-correction procedure, resulting in more realistic indices than those computed from the original GCM data. We find that the level of projected changes in the indices scales well with the projected increase in the global mean temperature and is insensitive to the emission scenarios. At the global warming level about 2.1 °C above pre-industrial (corresponding to the multi-model ensemble mean for 2031-2050 under the RCP8.5 scenario), there is almost complete model agreement on the sign of projected changes in temperature indices for every region in Canada. This includes projected increases in extreme high temperatures and cooling demand, growing season length, and decrease in heating demand. Models project much larger changes in temperature indices at the higher 4.5 °C global warming level (corresponding to 2081-2100 under the RCP8.5 scenario). Models also project an increase in total precipitation, in the frequency and intensity of precipitation, and in extreme precipitation. Uncertainty is high in precipitation projections, with the result that models do not fully agree on the sign of changes in most regions even at the 4.5 °C global warming level. [ABSTRACT FROM AUTHOR]

Heavy precipitation exerts strong societal and economic impacts, including flooding, and these precipitation events are projected to increase under anthropogenic warming. The United Nations Framework Convention on Climate Change (UNFCCC) Paris Agreement signed in December 2015 aims to limit the global average temperature rise to below 2 °C above preindustrial levels, with an added goal of limiting temperature increases to 1.5 °C. There remains a major knowledge gap related to our understanding of changes in heavy precipitation under the 1.5 and 2 °C warming targets. Here, we investigate the changes in heavy precipitation events with the Community Earth System Model (CESM) climate experiments using the scenarios consistent with the 1.5 and 2 °C temperature targets. We find that the frequency of annual heavy precipitation at a global scale increases in both 1.5 and 2 °C scenarios until around 2070, after which the magnitudes of the trend become much weaker or even negative. Overall, the annual frequency of heavy precipitation across the globe is similar between 1.5 and 2 °C for the period 2006-2035, and the changes in heavy precipitation in individual seasons are consistent with those for the entire year. The frequency of heavy precipitation in the 2 °C experiments is higher than for the 1.5 °C experiment after the late 2030s, particularly for the period 2071-2100. While the results of both experiments indicate that the warming targets in the Paris Agreement, if met, would be effective in reducing the frequency of heavy precipitation (2 °C target minus 1.5 °C target), they also suggest a lower risk of global heavy precipitation under the 1.5 °C target of about 33% for the period 2071-2100. [ABSTRACT FROM AUTHOR]

We investigate simulated hydrological extremes (i.e., high and low flows) under the present and future climatic conditions for five river basins worldwide: the Ganges, Lena, Niger, Rhine, and Tagus. Future projections are based on five GCMs and four emission scenarios. We analyse results from the HYPE, mHM, SWIM, VIC and WaterGAP3 hydrological models calibrated and validated to simulate each river. The use of different impact models and future projections allows for an assessment of the uncertainty of future impacts. The analysis of extremes is conducted for four different time horizons: reference (1981-2010), early-century (2006-2035), mid-century (2036-2065) and end-century (2070-2099). In addition, Sen's non-parametric estimator of slope is used to calculate the magnitude of trend in extremes, whose statistical significance is assessed by the Mann-Kendall test. Overall, the impact of climate change is more severe at the end of the century and particularly in dry regions. High flows are generally sensitive to changes in precipitation, however sensitivity varies between the basins. Finally, results show that conclusions in climate change impact studies can be highly influenced by uncertainty both in the climate and impact models, whilst the sensitivity to climate modelling uncertainty becoming greater than hydrological model uncertainty in the dry regions. [ABSTRACT FROM AUTHOR]

Future trends in debris flow activity are constructed based on bias-corrected climate change projections using two meteorological proxies: daily precipitation and Convective Available Potential Energy (CAPE) combined with specific humidity for two Alpine areas. Along with a comparison between proxies, future number of days with debris flows are analyzed with respect to different regional and global climate models, Representative Concentration Pathways (RCPs), and area for quantile mapping. Two different base periods are also analyzed, as debris flows were observed on only 6 (17) days between 1950 and 1979, yet on 18 (49) days between 1980 and 2009 for Fella River, NE Italy (Barcelonnette, SE French Alps). For both areas, future climate projections vary between no change up to an increase of 6.0 % per decade in days with debris flow occurrences towards the end of 21st century. In Barcelonnette, the base period and proxy have a bigger impact on the future number of debris flow days than the climate model or RCP used. In Fella River, the base period, RCP, and proxy used define the future range. Therefore the selection of proxy, base period and downscaling technique should be carefully considered for future climate change impact studies concerning debris flow activity and associated fast-moving landslides. [ABSTRACT FROM AUTHOR]

The study focuses on the analysis of extreme precipitation events of the present and future climate over southern Africa. Parametric and non-parametric approaches are used to identify and analyse these extreme events in data from the Coordinated Regional Climate Downscaling Experiment (CORDEX) models. The performance of the global climate model (GCM) forced regional climate model (RCM) simulations shows that the models are able to capture the observed climatological spatial patterns of the extreme precipitation. It is also shown that the downscaling of the present climate are able to add value to the performance of GCMs over some areas depending on the metric used. The added value over GCMs justifies the additional computational effort of RCM simulation for the generation of relevant climate information for regional application. In the climate projections for the end of twenty-first Century (2069-2098) relative to the reference period (1976-2005), annual total precipitation is projected to decrease while the maximum number of consecutive dry days increases. Maximum 5-day precipitation amounts and 95th percentile of precipitation are also projected to increase significantly in the tropical and sub-tropical regions of southern Africa and decrease in the extra-tropical region. There are indications that rainfall intensity is likely to increase. This does not equate to an increase in total rainfall, but suggests that when it does rain, the intensity is likely to be greater. These changes are magnified under the RCP8.5 when compared with the RCP4.5 and are consistent with previous studies based on GCMs over the region. [ABSTRACT FROM AUTHOR]

Given the severe impacts of hot summers on human and natural systems, we attempt to quantify future changes in extreme hot summer frequency in China using the Coupled Model Intercomparison Project Phase 5 (CMIP5) projections. Unlike previous studies focusing on fixed future time slices, we investigate the changes as a function of global mean temperature (GMT) rise. Analyses show that extreme hot summers (June-July-August mean temperature higher than 90 % quantile of 1971-2000 climatology) are projected to occur at least 80 % of the time across China with a GMT rise of 2 °C. The fraction of land area with extreme hot summers becoming the norm (median of future summer temperatures exceed the extreme) will increase from ~15 % with 0.5 °C of GMT rise to ~97 % with 2.5 °C GMT rise, which is much greater than for the global land surface as a whole. A distinct spatial pattern of the GMT rise threshold over which the local extreme hot summer first becomes the norm is revealed. When averaged over the country, the GMT rise threshold is 0.96 °C. Earth system models exhibit comparable results to climate system models, but with a relatively larger spread. Further analysis shows that the concurrence of hot and dry summers will increase significantly with the spatial structure of responses depending on the definition of drying. The increase of concurrent hot and dry conditions will induce potential droughts which would be more severe than those induced by only precipitation deficits. [ABSTRACT FROM AUTHOR]

In this study we examine the performance of eight of the IPCC AR4 global coupled climate models used in the WCRP CMIP3 Multimodel Dataset, as well as their ensemble mean, in simulating annual indices of extreme temperature and precipitation climate events in South America. In this first part we focus on comparing observed and modeled mean values and interannual variability. Two extreme temperature indices based on minimum temperature (warm nights and frost days) and three indices of extreme precipitation (R95t, R10 and consecutive dry days), obtained both from meteorological stations during 1961–2000 and model outputs, were compared. The number of warm nights are better represented by models than the FD. The interannual variability pattern is also in good agreement with the observed values. For precipitation, the index that is best represented by the models is the R95t, which relates the extreme precipitation to local climate. The maximum of dryness observed over the central Argentinian Andes or the extensive dry season of the Amazon region could not be represented by any model. [ABSTRACT FROM AUTHOR]

Dynamical downscaling of global climate simulations is the most adequate tool to generate regional projections of climate change. This technique involves at least a present climate simulation and a simulation of a future scenario, usually at the end of the twenty first century. However, regional projections for a variety of scenarios and periods, the 2020s or the 2050s, are often required by the impact community. The pattern scaling technique is used to estimate information on climate change for periods and scenarios not simulated by the regional model. We based our study on regional simulations performed over southern South America for present climate conditions and two emission scenarios at the end of the twenty first century. We used the pattern scaling technique to estimate mean seasonal changes of temperature and precipitation for the 2020s and the 2050s. The validity of the scalability assumptions underlying the pattern scaling technique for estimating near future regional climate change scenarios over southern South America is assessed. The results show that the pattern scaling works well for estimating mean temperature changes for which the regional changes are linearly related to the global mean temperature changes. For precipitation changes, the validity of the scalability assumption is weaker. The errors of estimating precipitation changes are comparable to those inherent to the regional model and to the projected changes themselves. [ABSTRACT FROM AUTHOR]

We assess the likely changes in climate extremes under enhanced greenhouse gases over the southern extratropics, with emphasis in southern South America and sub-Antarctic seas, through the analysis of extreme indices measured from models participating in the IPCC 4th Assessment Report. We discuss how the anthropogenic climate change under A1B scenario influences both the patterns of mean change of extreme indices and the likelihood of occurrence of severe extreme indices. The likelihood of occurrence of a year with a large number of days with “warm” minimum temperatures is estimated to increase by a factor of 4 by the end of this century over most of the southern extratropics. By that time, the risk of “severe” precipitation intensity is projected to rise in most areas with the exception of the subtropical anticyclones, which experience particularly strong drying. Over the Southern Ocean this likelihood has increased to over 60%. Corresponding estimates of the changing likelihood for very long dry spells show a banded structure with positive ratios to the north of about 50° S and negative ratios in the sub Antarctic seas. In southern South America this risk about doubled between present and future climates. Then, we explore if the Southern Annular Mode influences the occurrence of severe extreme indices during the period 2070–2099. Its positive phase inhibits the extremely warm minimum temperatures in the Southern Ocean, with the exception of the eastern Bellingshausen Sea, and favors severe frost days to the north of the Ross Sea. Temperature indices show very little change induced by the SAM to the north of 50° S. Severe dry spells are inhibited during the positive phase along the sub Antarctic seas, while the mid-latitudes, including most of Patagonia, show the opposite behaviour. The Southern Ocean reveals a non-uniform distribution with both increases and decreases in the occurrence of heavier precipitation during positive SAM. [ABSTRACT FROM AUTHOR]