Climate-change effects on extreme precipitation in central Europe: uncertainties of scenarios based on regional climate models

Observations as well as most climate model simulations are generally in accord with the hypothesis that the hydrologic cycle should intensify and become highly volatile with the greenhouse-gas-induced climate change, although uncertainties of these projections as well as the spatial and seasonal variability of the changes are much larger than for temperature extremes. In this study, we examine scenarios of changes in extreme precipitation events in 24 future climate runs of ten regional climate models, focusing on a specific area of the Czech Republic (central Europe) where complex orography and an interaction of other factors governing the occurrence of heavy precipitation events result in patterns that cannot be captured by global models. The peaks-over-threshold analysis with increasing threshold censoring is applied to estimate multi-year return levels of daily rainfall amounts. Uncertainties in scenarios of changes for the late 21st century related to the inter-model and within-ensemble variability and the use of the SRES-A2 and SRES-B2 greenhouse gas emission scenarios are evaluated. The results show that heavy precipitation events are likely to increase in severity in winter and (with less agreement among models) also in summer. The inter-model and intra-model variability and related uncertainties in the pattern and magnitude of the change is large, but the scenarios tend to agree with precipitation trends recently observed in the area, which may strengthen their credibility. In most scenario runs, the projected change in extreme precipitation in summer is of the opposite sign than a change in mean seasonal totals, the latter pointing towards generally drier conditions in summer. A combination of enhanced heavy precipitation amounts and reduced water infiltration capabilities of a dry soil may severely increase peak river discharges and flood-related risks in this region.     

Impacts of Present and Future Climate Variability On Agriculture and Forestry in the Humid and Sub-Humid Tropics

Although there are different results from different studies, most assessments indicate that climate variability would have negative effects on agriculture and forestry in the humid and sub-humid tropics. Cereal crop yields would decrease generally with even minimal increases in temperature. For commercial crops, extreme events such as cyclones, droughts and floods lead to larger damages than only changes of mean climate. Impacts of climate variability on livestock mainly include two aspects; impacts on animals such as increase of heat and disease stress-related death, and impacts on pasture. As to forestry, climate variability would have negative as well as some positive impacts on forests of humid and sub-humid tropics. However, in most tropical regions, the impacts of human activities such as deforestation will be more important than climate variability and climate change in determining natural forest cover.     

中国台站冬夏季气温、降水的气候变化特征及其显著性检验

利用1951—2010年中国160站气温、降水资料,分析中国代表性台站冬季和夏季气温、降水的气候值及气候变率在前后30 a的差异,并对结果使用不同方法进行显著性检验。结果表明,季气温气候平均值的变化总体与全球增暖一致,以升温为主,但夏季在秦岭以南及长江中游地区出现显著局部变冷现象;季气温气候变率的变化相对较小,冬季总体不显著,夏季仅有少数台站显著。降水的气候变化总体不明显,季降水气候值变化的空间分布复杂,冬季南方地区、夏季东部地区总体增加,冬、夏季降水气候变率的变化均不显著。理论检验方法（t检验、F检验）与随机模拟方法（EMC法）的显著性检验结果,对气温的差别较小、对降水的差别较大,这与样本距平序列是否服从正态分布有关。EMC法可在确保样本统计特征不变的情况下,通过多次随机模拟,无需考虑其理论统计分布特征,使检验结果更为可靠。     

Greenhouse climatic change and flood damages,the implications

Most scenarios of greenhouse climate change are obtained from general circulation models. These provide poor information on changes to extreme events. It is therefore, difficult to convert changes of flood frequency into their impact on flood damages. The procedures for estimating urban flood losses are outlined. Australian case studies illustrate the possible effects of greenhouse-induced changes in comparison to the variability under current climate; changes in urban flood losses for small and large catchments; and the implications for dam design. In all cases, relatively small increases in flood frequency would cause significant increases in loss. The policy implications are outlined, it must not be assumed that the availability of more precise data on future flood frequencies will be matched by policy response in the field of flood plain management.     

The effect of changes in daily and interannual climatic variability on CERES-Wheat: A sensitivity study

We investigate the effect of changes in daily and interannual variability of temperature and precipitation on yields simulated by the CERES-Wheat model at two locations in the central Great Plains. Changes in variability were effected by adjusting parameters of the Richardson daily weather generator. Two types of changes in precipitation were created: one with both intensity and frequency changed; and another with change only in persistence. In both types mean total monthly precipitation is held constant. Changes in daily (and interannual) variability of temperature result in substantial changes in the mean and variability of simulated wheat yields. With a doubling of temperature variability, large reductions in mean yield and increases in variability of yield result primarily from crop failures due to winter kill at both locations. Reduced temperature variability has little effect. Changes in daily precipitation variability also resulted in substantial changes in mean and variability of yield. Interesting interactions of the precipitation variability changes with the contrasting base climates are found at the two locations. At one site where soil moisture is not limiting, mean yield decreased and variability of yield increased with increasing precipitation variability, whereas mean yields increased at the other location, where soil moisture is limiting. Yield changes were similar for the two different types of precipitation variability change investigated. Compared to an earlier study for the same locations wherein variability changes were effected by altering observed time series, and the focus was on interannual variability, the present results for yield changes are much more substantial. This study demonstrates the importance of taking into account change in daily (and interannual) variability of climate when analyzing the effect of climate change on crop yields.The National Center for Atmospheric Research is sponsored by the National Science Foundation.     

Changes in climate extremes, variability and signature on sub-Antarctic Marion Island

The ecological consequences of climate change are determined by many climate parameters, not just by the commonly investigated changes in mean temperature and rainfall. More comprehensive studies, including analyses of climate variability, extremes and aggregate changes in the climate system, can improve the understanding of the nature, and therefore possible consequences, of recent changes in climate. Here climate trends on the sub-Antarctic Marion Island are documented (between 1949 and 2003) in more detail than previously. Significant trends in biologically-relevant, and previously unexplored, parameters were observed, and the potential ecological consequences of these changes discussed. For example, the decline in precipitation experienced on the island comprises a trend for longer dry spells punctuated by fewer and smaller precipitation events. This more detailed understanding of the island’s drying trends enables more accurate predictions about its impacts, including, for example, particularly severe effects on plant species growing in soils with poor water-holding capacity. Therefore, in addition to changes in average conditions, more inclusive climate analyses should also examine trends in climatic variability and extremes, for individual climate parameters as well as for the climate system as a whole.     

An inter-comparison of regional climate models for Europe: model performance in present-day climate

The analysis of possible regional climate changes over Europe as simulated by 10 regional climate models within the context of PRUDENCE requires a careful investigation of possible systematic biases in the models. The purpose of this paper is to identify how the main model systematic biases vary across the different models. Two fundamental aspects of model validation are addressed here: the ability to simulate (1) the long-term (30 or 40 years) mean climate and (2) the inter-annual variability. The analysis concentrates on near-surface air temperature and precipitation over land and focuses mainly on winter and summer. In general, there is a warm bias with respect to the CRU data set in these extreme seasons and a tendency to cold biases in the transition seasons. In winter the typical spread (standard deviation) between the models is 1 K. During summer there is generally a better agreement between observed and simulated values of inter-annual variability although there is a relatively clear signal that the modeled temperature variability is larger than suggested by observations, while precipitation variability is closer to observations. The areas with warm (cold) bias in winter generally exhibit wet (dry) biases, whereas the relationship is the reverse during summer (though much less clear, coupling warm (cold) biases with dry (wet) ones). When comparing the RCMs with their driving GCM, they generally reproduce the large-scale circulation of the GCM though in some cases there are substantial differences between regional biases in surface temperature and precipitation.     

Atmosphere-land surface interactions and their influence on extreme rainfall and potential abrupt climate change over southern Africa

In a changing climate, changes in rainfall variability and, in particular, extreme rainfall events are likely to be highly significant for environmentally vulnerable regions such as southern Africa. It is generally accepted that sea-surface temperatures play an important role in modulating rainfall variability, thus the majority work to date has focused on these mechanisms. However past research suggests that land surface processes are also critical for rainfall variability. In particular, work has suggested that the atmosphere-land surface feedback has been important for past abrupt climate changes, such as those which occurred over the Sahara during the mid-Holocene or, more recently, the prolonged Sahelian drought. Therefore the primary aim of this work is to undertake idealised experiments using both a regional and global climate model, to test the sensitivity of rainfall variability to land surface changes over a location where such abrupt climate changes are projected to occur in the future, namely southern Africa. In one experiment, the desert conditions currently observed over southwestern Africa were extended to cover the entire subcontinent. This is based on past research which suggests a remobilisation of sand dune activity and spatial extent under various scenarios of future anthropogenic global warming. In the second experiment, savanna conditions were imposed over all of southern Africa, representing an increase in vegetation for most areas except the equatorial regions. The results suggest that a decrease in rainfall occurs in the desert run, up to 27% of total rainfall in the regional model (relative to the control), due to a reduction in available moisture, less evaporation, less vertical uplift and therefore higher near surface pressure. This result is consistent across both the regional and global model experiments. Conversely an increase in rainfall occurs in the savanna run, because of an increase in available moisture giving an increase in latent heat and therefore surface temperature, increasing vertical uplift and lowering near surface pressure. These experiments, however, are only preliminary, and form the first stage of a wider study into how the atmosphere-land surface feedback influences rainfall extremes over southern Africa in the past (when surface i.e. vegetation conditions were very different) and in the future under various scenarios of future climate change. Future work will examine how other climate models simulate the atmosphere-land surface feedback, using more realistic vegetation types based on past and future surface conditions.     

Seasonal variability of Indonesian rainfall in ECHAM4 simulations and in the reanalyses: The role of ENSO

Summary A study of the skill of the ECHAM version 4 atmospheric general circulation model and two reanalyses in simulating Indonesian rainfall is presented with comparisons to 30 years of rain gauge data. The reanalyses are those performed by the European Centre for Medium-Range Weather Forecasts and of the National Centers for Environmental Prediction jointly with National Center for Atmospheric Research. This study investigates the skill of the reanalyses and ECHAM4 with regard to three climate regions of Indonesia, the annual and interannual variability of rainfall and its responses to El Ni?o-Southern Oscillation (ENSO) events. The study is conducted at two spectral resolutions, T42 and T106. The skill of rainfall simulations in Indonesia depends on the region, month and season, and the distribution of land and sea. Higher simulation skills are confined to years with ENSO events. With the exception of the northwest region of Indonesia, the rainfall from June (Molucca) and July (south Indonesia) to November is influenced by ENSO, and is more sensitive to El Ni?o than La Ni?a events. Observations show that the Moluccan region is more sensitive to ENSO, receives a longer ENSO impact and receives the earliest ENSO impact in June, which continues through to December. It is found that the reanalyses and the climate model simulate seasonal variability better than monthly variability. The seasonal skill is highest in June/July/August, followed by September/October/November, December/January/February and March/April/May. The correlations usually break down in April (for monthly analysis) or in the boreal spring (for seasonal analysis). This period seems to act as a persistent barrier to Indonesian rainfall predictability and skill. In general, the performance of ECHAM4 is poor, but in ENSO sensitive regions and during ENSO events, it is comparable to the reanalyses.     

Uncertainty in climate change projections: the role of internal variability

Uncertainty in future climate change presents a key challenge for adaptation planning. In this study, uncertainty arising from internal climate variability is investigated using a new 40-member ensemble conducted with the National Center for Atmospheric Research Community Climate System Model Version 3 (CCSM3) under the SRES A1B greenhouse gas and ozone recovery forcing scenarios during 2000–2060. The contribution of intrinsic atmospheric variability to the total uncertainty is further examined using a 10,000-year control integration of the atmospheric model component of CCSM3 under fixed boundary conditions. The global climate response is characterized in terms of air temperature, precipitation, and sea level pressure during winter and summer. The dominant source of uncertainty in the simulated climate response at middle and high latitudes is internal atmospheric variability associated with the annular modes of circulation variability. Coupled ocean-atmosphere variability plays a dominant role in the tropics, with attendant effects at higher latitudes via atmospheric teleconnections. Uncertainties in the forced response are generally larger for sea level pressure than precipitation, and smallest for air temperature. Accordingly, forced changes in air temperature can be detected earlier and with fewer ensemble members than those in atmospheric circulation and precipitation. Implications of the results for detection and attribution of observed climate change and for multi-model climate assessments are discussed. Internal variability is estimated to account for at least half of the inter-model spread in projected climate trends during 2005–2060 in the CMIP3 multi-model ensemble.     

Scenarios of extreme temperature events

Based on the principles of the probability theory a statistical model has been developed assessing the likelihood of occurrence of extreme temperature events from the knowledge of the statistical characteristics of the daily temperature extremes. It is demonstrated that the probability of such events is more sensitive to changes in the variability of climate than to changes in its average. Further, this sensitivity increases at a nonlinear rate the more extreme the event. The applicability of the model has been verified by comparing the simulated frequencies of a large spectrum of temperature events with the observed numbers derived from a long time series of daily temperature extremes at Potsdam. Accordingly, the relative simulation errors increase significantly as the events become more extreme. A correction is possible, because most of these errors are systematic rather than random. Moreover, in accordance with the climate observations the simulations reveal statistically significant linear trends in the number of extreme events since the end of the last century. Local scenarios of extreme temperature events have been derived for the city of Berlin by considering both hypothetical new climate states and climate changes simulated by a General Circulation Model (GCM). As a consequence of an increase in the atmospheric concentration of greenhouse gases up to the end of the next century according to the IPCC Scenario A the repetition rate of extreme events in summer (e.g., hot days) is expected to rise considerably relative to the current climate. Moreover, in the winter season cold days will become extremely rare.     

USE OF A STOCHASTIC WEATHER GENERATOR IN THE DEVELOPMENT OF CLIMATE CHANGE SCENARIOS

Climate change scenarios with a high spatial and temporal resolution are required in the evaluation of the effects of climate change on agricultural potential and agricultural risk. Such scenarios should reproduce changes in mean weather characteristics as well as incorporate the changes in climate variability indicated by the global climate model (GCM) used. Recent work on the sensitivity of crop models and climatic extremes has clearly demonstrated that changes in variability can have more profound effects on crop yield and on the probability of extreme weather events than simple changes in the mean values. The construction of climate change scenarios based on spatial regression downscaling and on the use of a local stochastic weather generator is described. Regression downscaling translated the coarse resolution GCM grid-box predictions of climate change to site-specific values. These values were then used to perturb the parameters of the stochastic weather generator in order to simulate site-specific daily weather data. This approach permits the incorporation of changes in the mean and variability of climate in a consistent and computationally inexpensive way. The stochastic weather generator used in this study, LARS-WG, has been validated across Europe and has been shown to perform well in the simulation of different weather statistics, including those climatic extremes relevant to agriculture. The importance of downscaling and the incorporation of climate variability are demonstrated at two European sites where climate change scenarios were constructed using the UK Met. Office high resolution GCM equilibrium and transient experiments.     

The absence of memory in the climatic forcing of glaciers

Glaciers respond to both long-term, persistent climate changes as well as the year-to-year variability that is inherent to a constant climate. Distinguishing between these two causes of length change is important for identifying the true climatic cause of past glacier fluctuations. A key step in addressing this is to determine the relative importance of year-to-year variability in climate relative to more persistent climate fluctuations. We address this question for European climate using several long-term observational records: a century-long, Europe-wide atmospheric gridded dataset; longer-term instrumental measurements of summertime temperature where available (up to 250 years); and seasonal and annual records of glacier mass balance (between 30 and 50 years). After linear detrending of the datasets, we find that throughout Europe persistence in both melt-season temperature and annual accumulation is generally indistinguishable from zero. The main exception is in Southern Europe where a degree of interannual persistence can be identified in summertime temperatures. On the basis of this analysis, we conclude that year-to-year variability dominates the natural climate forcing of glacier fluctuations on timescales up to a few centuries.     

Ocean acidification and its impacts: an expert survey

The oceans moderate the rate and severity of climate change by absorbing massive amounts of anthropogenic CO₂ but this results in large-scale changes in seawater chemistry, which are collectively referred to as anthropogenic ocean acidification. Despite its potentially widespread consequences, the problem of ocean acidification has been largely absent from most policy discussions of CO₂ emissions, both because the science is relatively new and because the research community has yet to deliver a clear message to decision makers regarding its impacts. Here we report the results of the first expert survey in the field of ocean acidification. Fifty-three experts, who had previously participated in an IPCC workshop, were asked to assess 22 declarative statements about ocean acidification and its consequences. We find a relatively strong consensus on most issues related to past, present and future chemical aspects of ocean acidification: non-anthropogenic ocean acidification events have occurred in the geological past, anthropogenic CO₂ emissions are the main (but not the only) mechanism generating the current ocean acidification event, and anthropogenic ocean acidification that has occurred due to historical fossil fuel emissions will be felt for centuries. Experts generally agreed that there will be impacts on biological and ecological processes and biogeochemical feedbacks but levels of agreement were lower, with more variability across responses. Levels of agreement were higher for statements regarding calcification, primary production and nitrogen fixation than for those about impacts on foodwebs. The levels of agreement for statements pertaining to socio-economic impacts, such as impacts on food security, and to more normative policy issues, were relatively low.     

VULNERABILITY AND ADAPTIVE RESPONSE IN THE CONTEXT OF CLIMATE AND CLIMATE CHANGE

The paper explores the distinction between climate and climate change. Adaptation to current climate variability has been proposed as an additional way to approach adaptation to long-term climate change. In effect improved adaptation to current climate is a step in preparation for longer term climate change. International programs of research and assessment are separately organized to deal with natural disasters and climate change. There is no scientific concensus so far, that extreme events have changed in frequency on a world-wide basis, although some regional changes have occured. It is extremely unlikely that significant shifts in the means of weather distrbutions will take place without shifts in the tails. In some situations it may make more sense to focus on adaptation to extreme events and the tails of distributions. In other circumstances adaptation to the norms is the logical focus. The relationship between normal climate and climate change is examined in terms of single and complex variables and phenomena. It is proposed that the research communities studying adaptation to extreme events and adaptation to climate change work more closely together, perhaps in a newly organized joint research program.     

Learning about parameter and structural uncertainty in carbon cycle models

Uncertainty in the response of the global carbon cycle to anthropogenic emissions plays a key role in assessments of potential future climate change and response strategies. We investigate how fast this uncertainty might change as additional data on the global carbon budget becomes available over the twenty-first century. Using a simple global carbon cycle model and focusing on both parameter and structural uncertainty in the terrestrial sink, we find that additional global data leads to substantial learning (i.e., changes in uncertainty) under some conditions but not others. If the model structure is assumed known and only parameter uncertainty is considered, learning is rather limited if observational errors in the data or the magnitude of unexplained natural variability are not reduced. Learning about parameter values can be substantial, however, when errors in data or unexplained variability are reduced. We also find that, on the one hand, uncertainty in the model structure has a much bigger impact on uncertainty in projections of future atmospheric composition than does parameter uncertainty. But on the other, it is also possible to learn more about the model structure than the parameter values, even from global budget data that does not improve over time in terms of its associated errors. As an example, we illustrate how one standard model structure, if incorrect, could become inconsistent with global budget data within 40 years. The rate of learning in this analysis is affected by the choice of a relatively simple carbon cycle model, the use of observations only of global emissions and atmospheric concentration, and the assumption of perfect autocorrelation in observational errors and variability. Future work could usefully improve the approach in each of these areas.     

Effects of climate change and extreme events on forest communities in the European North

The European north is increasingly affected by changes in climate and climate variability. These changes and their causes are global in scope but specific impacts vary considerably between different regions. Recent incidents and events show that forest-resource based regions have difficulties in alleviating adverse effects of these changes. Also, the future socio-economic impact is to date unexplored. Norrbotten in Sweden, Lappi in Finland and Arkhangelsk oblast in Russia are regions that differ significantly in terms of their socio-economic characteristics and capacities. A modified employment multiplier model is used to predict future changes. Scenarios of changing forest resources provide quantitative estimations of the sensitivity of regional employment. These estimates are used to assess and discuss the adaptive capacities of the regions. Results show that Arkhangelsk oblast is more vulnerable to climate variability than Norrbotten and Lappi. This is due to the continued dependency on natural resources in combination with different capacities to counteract negative effects or to take advantage of the opportunities offered by climate change in this region.     

Assessment of a climate model to reproduce rainfall variability and extremes over Southern Africa

It is increasingly accepted that any possible climate change will not only have an influence on mean climate but may also significantly alter climatic variability. A change in the distribution and magnitude of extreme rainfall events (associated with changing variability), such as droughts or flooding, may have a far greater impact on human and natural systems than a changing mean. This issue is of particular importance for environmentally vulnerable regions such as southern Africa. The sub-continent is considered especially vulnerable to and ill-equipped (in terms of adaptation) for extreme events, due to a number of factors including extensive poverty, famine, disease and political instability. Rainfall variability and the identification of rainfall extremes is a function of scale, so high spatial and temporal resolution data are preferred to identify extreme events and accurately predict future variability. The majority of previous climate model verification studies have compared model output with observational data at monthly timescales. In this research, the assessment of ability of a state of the art climate model to simulate climate at daily timescales is carried out using satellite-derived rainfall data from the Microwave Infrared Rainfall Algorithm (MIRA). This dataset covers the period from 1993 to 2002 and the whole of southern Africa at a spatial resolution of 0.1° longitude/latitude. This paper concentrates primarily on the ability of the model to simulate the spatial and temporal patterns of present-day rainfall variability over southern Africa and is not intended to discuss possible future changes in climate as these have been documented elsewhere. Simulations of current climate from the UK Meteorological Office Hadley Centre’s climate model, in both regional and global mode, are firstly compared to the MIRA dataset at daily timescales. Secondly, the ability of the model to reproduce daily rainfall extremes is assessed, again by a comparison with extremes from the MIRA dataset. The results suggest that the model reproduces the number and spatial distribution of rainfall extremes with some accuracy, but that mean rainfall and rainfall variability is under-estimated (over-estimated) over wet (dry) regions of southern Africa.