Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River, China

A statistical downscaling method (SDSM) was evaluated by simultaneously downscaling air temperature, evaporation, and precipitation in Haihe River basin, China. The data used for evaluation were large-scale atmospheric data encompassing daily NCEP/NCAR reanalysis data and the daily mean climate model results for scenarios A2 and B2 of the HadCM3 model. Selected as climate variables for downscaling were measured daily mean air temperature, pan evaporation, and precipitation data (1961–2000) from 11 weather stations in the Haihe River basin. The results obtained from SDSM showed that: (1) the pattern of change in and numerical values of the climate variables can be reasonably simulated, with the coefficients of determination between observed and downscaled mean temperature, pan evaporation, and precipitation being 99%, 93%, and 73%, respectively; (2) systematic errors existed in simulating extreme events, but the results were acceptable for practical applications; and (3) the mean air temperature would increase by about 0.7°C during 2011~2040; the total annual precipitation would decrease by about 7% in A2 scenario but increase by about 4% in B2 scenario; and there were no apparent changes in pan evaporation. It was concluded that in the next 30 years, climate would be warmer and drier, extreme events could be more intense, and autumn might be the most distinct season among all the changes.     

Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania

Summary Regional climate model and statistical downscaling procedures are used to generate winter precipitation changes over Romania for the period 2071–2100 (compared to 1961–1990), under the IPCC A2 and B2 emission scenarios. For this purpose, the ICTP regional climate model RegCM is nested within the Hadley Centre global atmospheric model HadAM3H. The statistical downscaling method is based on the use of canonical correlation analysis (CCA) to construct climate change scenarios for winter precipitation over Romania from two predictors, sea level pressure and specific humidity (either used individually or together). A technique to select the most skillful model separately for each station is proposed to optimise the statistical downscaling signal. Climate fields from the A2 and B2 scenario simulations with the HadAM3H and RegCM models are used as input to the statistical downscaling model. First, the capability of the climate models to reproduce the observed link between winter precipitation over Romania and atmospheric circulation at the European scale is analysed, showing that the RegCM is more accurate than HadAM3H in the simulation of Romanian precipitation variability and its connection with large-scale circulations. Both models overestimate winter precipitation in the eastern regions of Romania due to an overestimation of the intensity and frequency of cyclonic systems over Europe. Climate changes derived directly from the RegCM and HadAM3H show an increase of precipitation during the 2071–2100 period compared to 1961–1990, especially over northwest and northeast Romania. Similar climate change patterns are obtained through the statistical downscaling method when the technique of optimum model selected separately for each station is used. This adds confidence to the simulated climate change signal over this region. The uncertainty of results is higher for the eastern and southeastern regions of Romania due to the lower HadAM3H and RegCM performance in simulating winter precipitation variability there as well as the reduced skill of the statistical downscaling model.     

Climate change scenarios for precipitation and potential evapotranspiration over central Belgium

In this article, we examine climate model estimations for the future climate over central Belgium. Our analysis is focused mainly on two variables: potential evapotranspiration (PET) and precipitation. PET is calculated using the Penman equation with parameters appropriately calibrated for Belgium, based on RCM data from the European project PRUDENCE database. Next, we proceed into estimating the model capacity to reproduce the reference climate for PET and precipitation. The same analysis for precipitation is also performed based on GCM data from the IPCC AR4 database. Then, the climate change signal is evaluated over central Belgium using RCM and GCM simulations based on several SRES scenarios. The RCM simulations show a clear shift in the precipitation pattern with an increase during winter and a decrease during summer. However, the inclusion of another set of SRES scenarios from the GCM simulations leads to a less clear climate change signal.     

Climate change scenarios of precipitation extremes in Central Europe from ENSEMBLES regional climate models

The study examines future scenarios of precipitation extremes over Central Europe in an ensemble of 12 regional climate model (RCM) simulations with the 25-km resolution, carried out within the European project ENSEMBLES. We apply the region-of-influence method as a pooling scheme when estimating distributions of extremes, which consists in incorporating data from a ‘region’ (set of gridboxes) when fitting an extreme value distribution in any single gridbox. The method reduces random variations in the estimates of parameters of the extreme value distribution that result from large spatial variability of heavy precipitation. Although spatial patterns differ among the models, most RCMs simulate increases in high quantiles of precipitation amounts when averaged over the area for the late-twenty-first century (2070–2099) climate in both winter and summer. The sign as well as the magnitude of the projected change vary only little for individual parts of the distribution of daily precipitation in winter. In summer, on the other hand, the projected changes increase with the quantile of the distribution in all RCMs, and they are negative (positive) for parts of the distribution below (above) the 98% quantile if averaged over the RCMs. The increases in precipitation extremes in summer are projected in spite of a pronounced drying in most RCMs. Although a rather general qualitative agreement of the models concerning the projected changes of precipitation extremes is found in both winter and summer, the uncertainties in climate change scenarios remain large and would likely further increase considerably if a more complete ensemble of RCM simulations driven by a larger suite of global models and with a range of possible scenarios of the radiative forcing is available.     

Refinement of dynamically downscaled precipitation and temperature scenarios

A method for adjusting dynamically downscaled precipitation and temperature scenarios representing specific sites is presented. The method reproduces mean monthly values and standard deviations based on daily observations. The trend obtained in the regional climate model both for temperature and precipitation is maintained, and the frequency of modelled and observed rainy days shows better agreement. Thus, the method is appropriate for tailoring dynamically downscaled temperature and precipitation values for climate change impact studies. One precipitation and temperature scenario dynamically downscaled with HIRHAM from the Atmospheric-Ocean General Circulation Model at the Max-Planck Institute in Hamburg, ECHAM4/OPYC4 GSDIO with emission scenario IS92a, is chosen to illustrate the adjustment method.     

Multi-year fluctuations of temperature and precipitation: The gray area of climate change

The issue of whether the secular climate (twentieth century) is stationary or changing to some new semi-permanent state is clouded by the presence of so-called climate fluctuations. The twentieth century climate record of the United States reveals a substantial number of decadal fluctuations which occur in all seasons for both temperature and precipitation. Recent examples of such behavior include changes in winter and summer temperature variability and increases in transition season precipitation. Statistical evidence suggests that a substantial portion of these fluctuations, even those which are remarkably unusual, are merely manifestations of a stochastic process which possesses weak year-to-year persistence as viewed from an a posteriori perspective. The implications of this result are particularly important with respect to the formulation of physical causes of the fluctuations. The results emphasize the desirability of well-founded clearly-stated a priori theories of climate change as well as the limited usefulness of widely used climate normals.     

A study on combining global and regional climate model results for generating climate scenarios of temperature and precipitation for the Netherlands

Climate scenarios for the Netherlands are constructed by combining information from global and regional climate models employing a simplified, conceptual framework of three sources (levels) of uncertainty impacting on predictions of the local climate. In this framework, the first level of uncertainty is determined by the global radiation balance, resulting in a range of the projected changes in the global mean temperature. On the regional (1,000–5,000 km) scale, the response of the atmospheric circulation determines the second important level of uncertainty. The third level of uncertainty, acting mainly on a local scale of 10 (and less) to 1,000 km, is related to the small-scale processes, like for example those acting in atmospheric convection, clouds and atmospheric meso-scale circulations—processes that play an important role in extreme events which are highly relevant for society. Global climate models (GCMs) are the main tools to quantify the first two levels of uncertainty, while high resolution regional climate models (RCMs) are more suitable to quantify the third level. Along these lines, results of an ensemble of RCMs, driven by only two GCM boundaries and therefore spanning only a rather narrow range in future climate predictions, are rescaled to obtain a broader uncertainty range. The rescaling is done by first disentangling the climate change response in the RCM simulations into a part related to the circulation, and a residual part which is related to the global temperature rise. Second, these responses are rescaled using the range of the predictions of global temperature change and circulation change from five GCMs. These GCMs have been selected on their ability to simulate the present-day circulation, in particular over Europe. For the seasonal means, the rescaled RCM results obey the range in the GCM ensemble using a high and low emission scenario. Thus, the rescaled RCM results are consistent with the GCM results for the means, while adding information on the small scales and the extremes. The method can be interpreted as a combined statistical–dynamical downscaling approach, with the statistical relations based on regional model output.     

Shoreline evolution under climate change wave scenarios

This paper investigates changes in shoreline evolution caused by changes in wave climate. In particular, a number of nearshore wave climate scenarios corresponding to a ??present?? (1961?C1990) and a future time-slice (2071?C2100) are used to drive a beach evolution model to determine monthly and seasonal statistics. To limit the number of variables, an idealised shoreline segment is adopted. The nearshore wave climate scenarios are generated from wind climate scenarios through point wave hindcast and inshore transformation. The original wind forcing comes from regional climate change model experiments of different resolutions and/or driving global climate models, representing different greenhouse-gas emission scenarios. It corresponds to a location offshore the south central coast of England. Hypothesis tests are applied to map the degree of evidence of future change in wave and shoreline statistics relative to the present. Differential statistics resulting from different global climate models and future emission scenarios are also investigated. Further, simple, fast, and straightforward methods that are capable of accommodating a great number of climate change scenarios with limited data reduction requirements are proposed to tackle the problem under consideration. The results of this study show that there are statistically significant changes in nearshore wave climate conditions and beach alignment between current and future climate scenarios. Changes are most notable during late summer for the medium-high future emission scenario and late winter for the medium-low. Despite frequent disagreement between global climate change models on the statistical significance of a change, all experiments agreed in future seasonal trends. Finally, a point of importance for coastal management, material shoreline changes are generally linked to significant changes in future wave direction rather than wave height.     

气候变化背景下江苏河蟹养殖高温热害风险评估

利用江苏省及周边共85个气象站观测资料,筛选出夏季高温强度、持续时间、降水量和日照时数作为高温热害的关键气象因子,构建高温热害综合指数。在此基础上,利用高温热害发生频率和河蟹因灾死亡率加权建立风险评估模型,将河蟹高温热害风险划分为三个等级,结合地形和土壤的适宜性,最终得出江苏河蟹高温热害风险区划图。结果发现,河蟹高温热害的高风险区位于以高淳为中心的江苏西南部,沿淮和淮北东部沿海地区风险值最低,淮北西部—沿江东部的风险值介于二者之间。年代际高风险区面积有逐渐扩大趋势,1991年以来已外扩到沿江和苏南大部分地区,达到历史极值。河蟹高温热害风险增大,需加强防范。     

Climate change scenarios for precipitation extremes in Portugal

Precipitation indices are commonly used as climate change indicators. Considering four Climate Variability and Predictability-recommended indices, this study assesses possible changes in their spatial patterns over Portugal under future climatic conditions. Precipitation data from the regional climate model Consortium for Small-Scale Modelling–Climate version of the Local Model (CCLM) ensemble simulations with ECHAM5/MPI-OM1 boundary conditions are used for this purpose. For recent–past, medians and probability density functions of the CCLM-based indices are validated against station-based and gridded observational dataset from ENSEMBLES-based (gridded daily precipitation data provided by the European Climate Assessment & Dataset project) indices. It is demonstrated that the model is able to realistically reproduce not only precipitation but also the corresponding extreme indices. Climate change projections for 2071–2100 (A1B and B1 SRES scenarios) reveal significant decreases in total precipitation, particularly in autumn over northwestern and southern Portugal, though changes exhibit distinct local and seasonal patterns and are typically stronger for A1B than for B1. The increase in winter precipitation over northeastern Portugal in A1B is the most important exception to the overall drying trend. Contributions of extreme precipitation events to total precipitation are also expected to increase, mainly in winter and spring over northeastern Portugal. Strong projected increases in the dry spell lengths in autumn and spring are also noteworthy, giving evidence for an extension of the dry season from summer to spring and autumn. Although no coupling analysis is undertaken, these changes are qualitatively related to modifications in the large-scale circulation over the Euro-Atlantic area, more specifically to shifts in the position of the Azores High and associated changes in the large-scale pressure gradient over the area.     

Increasing impacts of climate change upon ecosystems with increasing global mean temperature rise

In a meta-analysis we integrate peer-reviewed studies that provide quantified estimates of future projected ecosystem changes related to quantified projected local or global climate changes. In an advance on previous analyses, we reference all studies to a common pre-industrial base-line for temperature, employing up-scaling techniques where necessary, detailing how impacts have been projected on every continent, in the oceans, and for the globe, for a wide range of ecosystem types and taxa. Dramatic and substantive projected increases of climate change impacts upon ecosystems are revealed with increasing annual global mean temperature rise above the pre-industrial mean (ΔT_g). Substantial negative impacts are commonly projected as ΔT_g reaches and exceeds 2°C, especially in biodiversity hotspots. Compliance with the ultimate objective of the United Nations Framework Convention on Climate Change (Article 2) requires that greenhouse gas concentrations be stabilized within a time frame “sufficient to allow ecosystems to adapt naturally to climate change”. Unless ΔT_g is constrained to below 2°C at most, results here imply that it will be difficult to achieve compliance. This underscores the need to limit greenhouse gas emissions by accelerating mitigation efforts and by protecting existing ecosystems from greenhouse-gas producing land use change processes such as deforestation.     

An analysis of regional climate change in Switzerland

Summary An analysis of daily climatological data covering the period from 1901 to 1992 for four locations in Switzerland (Zurich, Lugano, Davos, and Säntis) has been made. The study has highlighted the fact that climate change this century is characterized by increases in minimum temperatures of about 2 K, a more modest increase in maximum temperatures (in some instances a decrease of maxima in the latter part of the record), little trend in the precipitation data, and a general decrease of sunshine duration through to the mid 1980s. The interannual variability is generally large, and filtering of the data to remove high-frequency noise shows that the regional climate undergoes a series of fluctuations of between 8 and 20 years' duration. The temperature change over this century is of greater magnitude than the global temperature changes published in the literature, reflecting an amplification of the global signal in the Alpine region; warming has been most intense in the 1940s, followed by the 1980s; the cooling which intervened from the 1950s to the late 1970s was not sufficient to offset the warming in the middle of the century.Pressure statistics have been compiled as a means of providing a link between the regional-scale climatological variables and the synoptic, supra-regional scale. These statistics show that pressure also exhibits a number of decadal-scale fluctuations, with the appearance of a new and anomalous behavior in the 1980s; in this decade, pressure reaches annual average values far higher than at other times this century. The pressure field is well correlated with the North Atlantic Oscillation (NAO) Index for distinct periods of the record (1931–1950 and 1971–1990) and is almost decorrelated from the NAO Index for the other decades of the century; this is indicative of transition from one climatic régime to another, dominated by zonal flow when the correlation with the NAO Index is high. In the 1980s, when zonal flow over the North Atlantic is strong, episodes of persistent, anomalously high pressures (blocking highs) are seen to occur over Switzerland, particularly during the winter season. The difference between the zonal and non-zonal régimes is particularly marked between the decade of the 1950s and that of the 1980s.The impact of this change between the 1950s and the 1980s on a number of climatological variables has been investigated statistically in order to provide an illustration of the manner in which changes in synoptic régimes (i.e., climate change) impacts upon climate characteristics on a regional scale. The analysis shows that temperature, precipitation, snow depth, and sunshine duration are indeed sensitive to large-scale influences; not only can yearly mean changes be quantified, but also seasonal and monthly fluctuations.With 26 Figures     

Extreme precipitation over the Maritime Alps and associated weather regimes simulated by a regional climate model: Present-day and future climate scenarios

Summary We use the regional climate model RegCM nested within time-slice atmospheric general circulation model experiments to investigate the possible changes of intense and extreme precipitation over the French Maritime Alps in response to global climate change. This is a region with complex orography where heavy and/or extended precipitation episodes induced catastrophic floods during the last decades. Output from a 30-year simulation of present-day climate (1961–1990) is first analysed and compared with NCEP reanalysed 700 hPa geopotential heights (Z700) and daily precipitation observations from the Alpine Precipitation Climatology (1966–1999). Two simulations under forcing from the A2 and B2 IPCC emission scenarios for the period 2071–2100 are used to investigate projected changes in extreme precipitation for our region of interest. In general, the model overestimates the annual cycle of precipitation. The climate change projections show some increase of precipitation, mostly outside the warm period for the B2 scenario, and some increase in the variability of the annual precipitation totals for the A2 scenario. The model reproduces the main observed patterns of the spatial leading EOFs in the Z700 field over the Atlantic-European domain. The simulated large scale circulation (LSC) variability does not differ significantly from that of the reanalysis data provided the EOFs are computed on the same domain. Two similar clusters of LSC corresponding to heavy precipitation days were identified for both simulated and observed data and their patterns do not change significantly in the climate change scenarios. The analysis of frequency histograms of extreme indices shows that the control simulation systematically underestimates the observed heavy precipitation expressed as the 90^th percentile of rainday amounts in all seasons except summer and better reproduces the greatest 5-day precipitation accumulation. The main hydrological changes projected for the Maritime Alps consist of an increase of most intense wet spell precipitation during winters for both scenarios and during autumn for the B2 scenario. Case studies of heavy precipitation events show that the RegCM is capable to reproduce the physical mechanisms responsible for heavy precipitation over our region of interest.     

Dynamical downscaling simulation and projection for mean and extreme temperature and precipitation over central Asia

Climate Dynamics - As a typical arid and semi-arid area, central Asia (CA) has scarce water resources and fragile ecosystems that are particularly sensitive and vulnerable to climate change. In...     

Dynamic adaptive pathways in downscaled climate change scenarios

The parallel scenario process enables characterization of climate-related risks and response options to climate change under different socio-economic futures and development prospects. The process is based on representative concentration pathways, shared socio-economic pathways, and shared policy assumptions. Although this scenario architecture is a powerful tool for evaluating the intersection of climate and society at the regional and global level, more specific context is needed to explore and understand risks, drivers, and enablers of change at the national and local level. We discuss the need for a stronger recognition of such national-scale characteristics to make climate change scenarios more relevant at the national and local scale, and propose ways to enrich the scenario architecture with locally relevant details that enhance salience, legitimacy, and credibility for stakeholders. Dynamic adaptive pathways are introduced as useful tools to draw out which elements of a potentially infinite scenario space connect with decision-relevant aspects of particular climate-related and non-climate-related risks and response options. Reviewing adaptation pathways for New Zealand case studies, we demonstrate how this approach could bring the global-scale scenario architecture within reach of local-scale decision-making. Such a process would enhance the utility of scenarios for mapping climate-related risks and adaptation options at the local scale, involving appropriate stakeholder involvement.     

Farmer perceptions of climate change: Associations with observed temperature and precipitation trends,irrigation, and climate beliefs

How individuals perceive climate change is linked to whether individuals support climate policies and whether they alter their own climate-related behaviors, yet climate perceptions may be influenced by many factors beyond local shifts in weather. Infrastructure designed to control or regulate natural resources may serve as an important lens through which people experience climate, and thus may influence perceptions. Likewise, perceptions may be influenced by personal beliefs about climate change and whether it is human-induced. Here we examine farmer perceptions of historical climate change, how perceptions are related to observed trends in regional climate, how perceptions are related to the presence of irrigation infrastructure, and how perceptions are related to beliefs and concerns about climate change. We focus on the regions of Marlborough and Hawke’s Bay in New Zealand, where irrigation is utilized on the majority of cropland. Data are obtained through analysis of historical climate records from local weather stations, interviews (n = 20), and a farmer survey (n = 490). Across both regions, no significant historical trends in annual precipitation and summer temperatures since 1980 are observed, but winter warming trends are significant at around 0.2–0.3 °C per decade. A large fraction of farmers perceived increases in annual rainfall despite instrumental records indicating no significant trends, a finding that may be related to greater perceived water availability associated with irrigation growth. A greater fraction of farmers perceived rainfall increases in Marlborough, where irrigation growth has been most substantial. We find those classes of farmers more likely to have irrigation were also significantly more likely to perceive an increase in annual rainfall. Furthermore, we demonstrate that perceptions of changing climate – regardless of their accuracy – are correlated with increased belief in climate change and an increased concern for future climate impacts. Those farmers that believe climate change is occurring and is human induced are more likely to perceive temperature increases than farmers who believe climate change is not occurring and is not human induced. These results suggest that perceptions are influenced by a variety of personal and environmental factors, including infrastructure, which may in turn alter decisions about climate adaptation.