Future extreme events in European climate: an exploration of regional climate model projections

This paper compares how well satellite versus weather station measurements of climate predict agricultural performance in Brazil, India, and the United States. Although weather stations give accurate measures of ground conditions, they entail sporadic observations that require interpolation where observations are missing. In contrast, satellites have trouble measuring some ground phenomenon such as precipitation but they provide complete spatial coverage of various parameters over a landscape. The satellite temperature measurements slightly outperform the interpolated ground station data but the precipitation ground measurements generally outperform the satellite surface wetness index. In India, the surface wetness index outperforms station precipitation but this may be reflecting irrigation, not climate. The results suggest that satellites provide promising measures of temperature but that ground station data may still be preferred for measuring precipitation in rural settings.     

Potential impacts of afforestation on climate change and extreme events in Nigeria

Afforestation is usually thought as a good approach to mitigate impacts of warming over a region. This study presents an argument that afforestation may have bigger impacts than originally thought by previous studies. The study investigates the impacts of afforestation on future climate and extreme events in Nigeria, using a regional climate model (RegCM3), forced with global climate model simulations. The impacts of seven afforestation options on the near future (2031–2050, under A1B scenario) climate and the extreme events are investigated. RegCM3 replicates essential features in the present-day (1981–2000) climate and the associated extreme events, and adequately simulates the seasonal variations over the ecological zones in the country. However, the model simulates the seasonal climate better over the northern ecological zones than over the southern ecological zones. The simulated spatial distribution of the extreme events agrees well with the observation, though the magnitude of the simulated events is smaller than the observed. The study shows that afforestation in Nigeria could have both positive and negative future impacts on the climate change and extreme events in the country. While afforestation reduces the projected global warming and enhances rainfall over the afforested area (and over coastal zones), it enhances the warming and reduces the rainfall over the north-eastern part of the country. In addition, the afforestation induces more frequent occurrence of extreme rainfall events (flooding) over the coastal region and more frequent occurrence of heat waves and droughts over the semi-arid region. The positive and negative impacts of the afforestation are not limited to Nigeria; they extend to the neighboring countries. While afforestation lowers the warming and enhances rainfall over Benin Republic, it increases the warming and lowers the rainfall over Niger, Chad and Cameroon. The result of the study has important implication for the ongoing climate change mitigation and adaptation efforts in Nigeria.     

Characterising climate change discourse on social media during extreme weather events

When extreme weather events occur, people often turn to social media platforms to share information, opinions and experiences. One of the topics commonly discussed is the role climate change may or may not have played in influencing an event. Here, we examine Twitter posts that mentioned climate change in the context of three high-magnitude extreme weather events – Hurricane Irene, Hurricane Sandy and Snowstorm Jonas – in order to assess how the framing of the topic and the attention paid to it can vary between events. We also examine the role that contextual factors can play in shaping climate change coverage on the platform. We find that criticism of climate change denial dominated during Irene, while political and ideological struggle frames dominated during Sandy. Discourse during Jonas was, in contrast, more divided between posts about the scientific links between climate change and the events, and posts contesting climate science in general. The focus on political and ideological struggle frames during Sandy reflects the event’s occurrence at a time when the Occupy movement was active and the 2012 US Presidential Election was nearing. These factors, we suggest, could also contribute to climate change being a more prominent discussion point during Sandy than during Irene or Jonas. The Jonas frames, meanwhile, hint at lesser public understanding of how climate change may influence cold weather events when compared with tropical storms. Overall, our findings demonstrate how event characteristics and short-term socio-political context can play a critical role in determining the lenses through which climate change is viewed.     

Assessing the vulnerability of wind energy to climate change and extreme events

This article presents a review of the status and basis of wind-generated electricity production, the state of knowledge regarding possible changes in the spatio-temporal characteristics of the wind resource and wind turbine operating conditions, the principal extreme events that are of relevance to the wind energy industry, and the major potential vulnerabilities of the wind energy industry to climate change, with a specific focus on extreme events. Generally, the magnitude of projected changes over Europe and the contiguous USA are within the ‘conservative’ estimates embedded within the Wind Turbine Design Standards. However, more research is needed to quantify (i) how global climate evolution may influence the operation of wind turbines outside these regions, (ii) events causing coincident extreme wind speeds, gusts, and vertical wind shear, and (iii) combined wind-wave loading on offshore turbines.     

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.     

Probabilistic projections of transient climate change

This paper describes a Bayesian methodology for prediction of multivariate probability distribution functions (PDFs) for transient regional climate change. The approach is based upon PDFs for the equilibrium response to doubled carbon dioxide, derived from a comprehensive sampling of uncertainties in modelling of surface and atmospheric processes, and constrained by multiannual mean observations of recent climate. These PDFs are sampled and scaled by global mean temperature predicted by a Simple Climate Model (SCM), in order to emulate corresponding transient responses. The sampled projections are then reweighted, based upon the likelihood that they correctly replicate observed historical changes in surface temperature, and combined to provide PDFs for 20 year averages of regional temperature and precipitation changes to the end of the twenty-first century, for the A1B emissions scenario. The PDFs also account for modelling uncertainties associated with aerosol forcing, ocean heat uptake and the terrestrial carbon cycle, sampled using SCM configurations calibrated to the response of perturbed physics ensembles generated using the Hadley Centre climate model HadCM3, and other international climate model simulations. Weighting the projections using observational metrics of recent mean climate is found to be as effective at constraining the future transient response as metrics based on historical trends. The spread in global temperature response due to modelling uncertainty in the carbon cycle feedbacks is determined to be about 65–80 % of the spread arising from uncertainties in modelling atmospheric, oceanic and aerosol processes of the climate system. Early twenty-first century aerosol forcing is found to be extremely unlikely to be less than ?1.7 W m^?2. Our technique provides a rigorous and formal method of combining several lines of evidence used in the previous IPCC expert assessment of the Transient Climate Response. The 10th, 50th and 90th percentiles of our observationally constrained PDF for the Transient Climate Response are 1.6, 2.0 and 2.4 °C respectively, compared with the 10–90 % range of 1.0–3.0 °C assessed by the IPCC.     

Long-term climate monitoring and extreme events

Problems with long-term monitoring of various extreme meteorological events (including tropical and extratropical cyclones, extreme winds, temperatures and precipitation, and mesoscale events) are examined. For many types of extreme events, the maintenance of long-term homogeneity of observations is more difficult than is the case for means of variables. In some cases, however, a strategy of using more than a single variable to define an event, along with the careful elimination of biases in the data, can provide quantitative information about trends. Special care needs to be taken with extreme events deduced from meteorological analyses, because changes in analysis and observation systems are certain to have affected extremes. Also, compositing of observations from more than one station, using differences in means (of temperature for instance) to produce a single long-term site, may not remove the biases in the extremes. These problems, along with ambiguities in defining extreme events, and difficulties in combining different analyses from different sites, complicate (and perhaps invalidate) attempts to determine whether extreme weather is becoming more frequent. The best that is likely to be achieved, even with increased emphasis on attaining the high-level of homogeneity necessary in the observations, is to monitor long-term variations in certain important extreme events, in select locations with high-quality data. Regional indices of important extreme events, selected on the basis of their damage potential and capable of adequate monitoring, may be established. If, in the future, we are to answer the question “Are extreme weather events becoming more frequent?”, we must establish and protect high-quality stations capable of monitoring the most important extreme events (perhaps with such regional indices), and ensure that changes affecting the recording of extreme events (e.g., changes in exposure) are meticulously documented.     

Identifying uncertainties in Arctic climate change projections

Wide ranging climate changes are expected in the Arctic by the end of the 21st century, but projections of the size of these changes vary widely across current global climate models. This variation represents a large source of uncertainty in our understanding of the evolution of Arctic climate. Here we systematically quantify and assess the model uncertainty in Arctic climate changes in two CO₂ doubling experiments: a multimodel ensemble (CMIP3) and an ensemble constructed using a single model (HadCM3) with multiple parameter perturbations (THC-QUMP). These two ensembles allow us to assess the contribution that both structural and parameter variations across models make to the total uncertainty and to begin to attribute sources of uncertainty in projected changes. We find that parameter uncertainty is an major source of uncertainty in certain aspects of Arctic climate. But also that uncertainties in the mean climate state in the 20th century, most notably in the northward Atlantic ocean heat transport and Arctic sea ice volume, are a significant source of uncertainty for projections of future Arctic change. We suggest that better observational constraints on these quantities will lead to significant improvements in the precision of projections of future Arctic climate change.     

Vulnerability of the oil and gas sector to climate change and extreme weather events

A changing climate and more frequent extreme weather events pose challenges to the oil and gas sector. Identifying how these changes will affect oil and gas extraction, transportation, processing, and delivery, and how these industries can adapt to or mitigate any adverse impacts will be vital to this sector’s supply security. This work presents an overview of the sector’s vulnerability to a changing climate. It addresses the potential for Natech hazards and proposes risk reduction measures, including mitigation and adaptation options. Assessment frameworks to ensure the safety of people, the environment, and investments in the oil and gas sector in the face of climate change are presented and their limitations discussed. It is argued that a comprehensive and systemic analysis framework for risk assessment is needed. The paper concludes that climate change and extreme weather events represent a real physical threat to the oil and gas sector, particularly in low-lying coastal areas and areas exposed to extreme weather events. The sector needs to take climate change seriously, assess its own vulnerability, and take appropriate measures to prevent or mitigate any potentially negative effects.     

Probabilistic climate change projections using neural networks

Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm^–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity.     

Climate change in the northeastern US: regional climate model validation and climate change projections

A high resolution regional climate model (RCM) is used to simulate climate of the recent past and to project future climate change across the northeastern US. Different types of uncertainties in climate simulations are examined by driving the RCM with different boundary data, applying different emissions scenarios, and running an ensemble of simulations with different initial conditions. Empirical orthogonal functions analysis and K-means clustering analysis are applied to divide the northeastern US region into four climatologically different zones based on the surface air temperature (SAT) and precipitation variability. The RCM simulations tend to overestimate SAT, especially over the northern part of the domain in winter and over the western part in summer. Statistically significant increases in seasonal SAT under both higher and lower emissions scenarios over the whole RCM domain suggest the robustness of future warming. Most parts of the northeastern US region will experience increasing winter precipitation and decreasing summer precipitation, though the changes are not statistically significant. The greater magnitude of the projected temperature increase by the end of the twenty-first century under the higher emissions scenario emphasizes the essential role of emissions choices in determining the potential future climate change.     

Detection of anthropogenic climate change using an atmospheric GCM

 Atmosphere-only general circulation models are shown to be a useful tool for detecting an anthropogenic effect on climate and understanding recent climate change. Ensembles of atmospheric runs are all forced with the same observed changes in sea surface temperatures and sea-ice extents but differ in terms of the combinations of anthropogenic effects included. Therefore, our approach aims to detect the `immediate' anthropogenic impact on the atmosphere as opposed to that which has arisen via oceanic feedbacks. We have adapted two well-used detection techniques, pattern correlations and fingerprints, and both show that near-decadal changes in the patterns of zonal mean upper air temperature are well simulated, and that it is highly unlikely that the observed changes could be accounted for by sea surface temperature variations and internal variability alone. Furthermore, we show that for zonally averaged upper air temperature, internal `noise' in the atmospheric model is small enough that a signal emerges from the data even on interannual time scales; this would not be possible in a coupled ocean-atmosphere general circulation model. Finally, although anthropogenic forcings have had a significant impact on global mean land surface temperature, we find that their influence on the pattern of local deviations about this mean is so far undetectable. In order to achieve this in the future, as the signal grows, it will also be important that the response of the Northern Hemisphere mid-latitude westerly flow to changing sea surface temperatures is well simulated in climate model detection studies. Received: 3 December 1999 / Accepted: 30 October 2000     

西藏冰湖溃决灾害事件极端气候特征

以1960年以来西藏境内已有记载的27次冰湖溃决灾害事件作为研究对象,基于西藏国家气象站点长时间序列（有效记录至今）日气温和日降雨数据,计算得到16个极端气温指数和6个极端降雨指数。通过主成分变换,提取综合极端气温指数和综合极端降雨指数,并进行历史（10年内对比）极端气候特征对比,获得冰湖溃决灾害发生当年及当月极端气候状态,结果表明西藏冰湖溃决灾害发生期（当年及当月）极端气候特征显著,反映极端气候状态对于激发西藏冰湖溃决灾害发生的重要贡献,具体表现为：(1) 67%（18次）的冰湖溃决事件发生当年综合极端气温指数和综合极端降雨指数均大于前期50%年份的综合极端气候指数,其中,13次灾害发生当年极端气候异常水平超过前期70%年份;(2)已有灾害暴发月份记载的25次冰湖溃决事件中,19次冰湖溃决事件发生当月极端气候指数异常偏高,11次冰湖溃决事件发生当月极端气温和极端降水均大于75%往年同期综合极端气候指数;(3)部分灾害事件如扎日错（1981年6月)、龙纠错溃决（2000年8月）等,灾害发生当年极端气温状态低于往年,而暴发当月综合极端气温指数和综合极端降雨指数均大于历史同期水平,表现为加剧状态;(4)所有冰湖溃决灾害发生当月的综合极端气温指数均高于往年同期指数,表明短历时极端气温事件对高原冰湖溃决灾害形成具有重要影响。     

Uncertain climate: An investigation into public scepticism about anthropogenic climate change

This study presents a detailed investigation of public scepticism about climate change in Britain using the trend, attribution, and impact scepticism framework of Rahmstorf (2004). The study found that climate scepticism is currently not widespread in Britain. Although uncertainty and scepticism about the potential impacts of climate change were fairly common, both trend and attribution scepticism were far less prevalent. It further showed that the different types of scepticism are strongly interrelated. Although this may suggest that the general public does not clearly distinguish between the different aspects of the climate debate, there is a clear gradation in prevalence along the Rahmstorf typology. Climate scepticism appeared particularly common among older individuals from lower socio-economic backgrounds who are politically conservative and hold traditional values; while it is less common among younger individuals from higher socio-economic backgrounds who hold self-transcendence and environmental values. The finding that climate scepticism is rooted in people's core values and worldviews may imply a coherent and encompassing sceptical outlook on climate change. However, attitudinal certainty appeared mainly concentrated in non-sceptical groups, suggesting that climate sceptical views are not held very firmly. Implications of the findings for climate change communication and engagement are discussed.     

基于MODIS数据的陕西地表温度监测

根据8年来跟踪调研成果,果树辨证管理技术就是一种能够适应恶劣气候变化的果树管理新学科和新技术系统,这种技术已经在苹果、桃、枣、梨、杏、李子、葡萄等果树进行实践,证明它是具有普遍意义的管理方法。特别是它控制营养生长、促进生殖生长的机制,改变了“桃三杏四梨五年”的概念,表现出生态、早产、高产、低投入、管理简化、劳动强度大大降低等诸多优势。     

Climate change in Central America and Mexico: regional climate model validation and climate change projections

Central America has high biodiversity, it harbors high-value ecosystems and it??s important to provide regional climate change information to assist in adaptation and mitigation work in the region. Here we study climate change projections for Central America and Mexico using a regional climate model. The model evaluation shows its success in simulating spatial and temporal variability of temperature and precipitation and also in capturing regional climate features such as the bimodal annual cycle of precipitation and the Caribbean low-level jet. A variety of climate regimes within the model domain are also better identified in the regional model simulation due to improved resolution of topographic features. Although, the model suffers from large precipitation biases, it shows improvements over the coarse-resolution driving model in simulating precipitation amounts. The model shows a dry bias in the wet season and a wet bias in the dry season suggesting that it??s unable to capture the full range of precipitation variability. Projected warming under the A2 scenario is higher in the wet season than that in the dry season with the Yucatan Peninsula experiencing highest warming. A large reduction in precipitation in the wet season is projected for the region, whereas parts of Central America that receive a considerable amount of moisture in the form of orographic precipitation show significant decreases in precipitation in the dry season. Projected climatic changes can have detrimental impacts on biodiversity as they are spatially similar, but far greater in magnitude, than those observed during the El Ni?o events in recent decades that adversely affected species in the region.     

Forecasting extreme events in the tropical Indian Ocean sector climate

Prospects for forecasting climate variability over the tropical Indian Ocean sector, specifically extreme positive events of the Indian Dipole Mode (IDM), with lead times of a season or more are investigated using the NASA Seasonal-to-Interannual Prediction Project (NSIPP) coupled-model system. The coupled system presents biases in its climatology over the Indian Ocean sector, which include (i) warmest sea-surface temperatures (SSTs) occurring in the central equatorial basin rather than on the eastside with the eastern (western) tropical SSTs up to 1 °C too cool (warm), (ii) a too northwest lying InterTropical Convergence Zonal over the ocean in boreal fall, (iii) a thermocline shallower (deeper) than observed west of Sumatra-Java (north of Madagascar), (iv) a delay of about a month in the onset (cessation) of the southwest (southeast) monsoon in the west (east) in boreal spring (fall). These biases affect the effectiveness of the SST-clouds-shortwave radiation negative feedback, the sensitivity of SST to wind-stress perturbations, and the character of equatorial coupled ocean-atmosphere modes. Despite these biases, ensemble hindcasts of the SST anomalies averaged over the eastern and western poles of the IDM for the decade 1993–2002, which included extreme positive events in 1994 and 1997/1998, are encouragingly good at 3-months lead. The onset of the 1997/1998-event is delayed by about a month, though the peak and decay are correctly timed. At 6-months lead-time, the forecast at the eastern pole deteriorates with either positive or negative false alarms generated each boreal fall. The forecast at the western pole remains good.     

Influence of SST biases on future climate change projections

We use a quantile-based bias correction technique and a multi-member ensemble of the atmospheric component of NCAR CCSM3 (CAM3) simulations to investigate the influence of sea surface temperature (SST) biases on future climate change projections. The simulations, which cover 1977?C1999 in the historical period and 2077?C2099 in the future (A1B) period, use the CCSM3-generated SSTs as prescribed boundary conditions. Bias correction is applied to the monthly time-series of SSTs so that the simulated changes in SST mean and variability are preserved. Our comparison of CAM3 simulations with and without SST correction shows that the SST biases affect the precipitation distribution in CAM3 over many regions by introducing errors in atmospheric moisture content and upper-level (lower-level) divergence (convergence). Also, bias correction leads to significantly different precipitation and surface temperature changes over many oceanic and terrestrial regions (predominantly in the tropics) in response to the future anthropogenic increases in greenhouse forcing. The differences in the precipitation response from SST bias correction occur both in the mean and the percent change, and are independent of the ocean?Catmosphere coupling. Many of these differences are comparable to or larger than the spread of future precipitation changes across the CMIP3 ensemble. Such biases can affect the simulated terrestrial feedbacks and thermohaline circulations in coupled climate model integrations through changes in the hydrological cycle and ocean salinity. Moreover, biases in CCSM3-generated SSTs are generally similar to the biases in CMIP3 ensemble mean SSTs, suggesting that other GCMs may display a similar sensitivity of projected climate change to SST errors. These results help to quantify the influence of climate model biases on the simulated climate change, and therefore should inform the effort to further develop approaches for reliable climate change projection.     

Uncertainty assessments of climate change projections over South America

This paper assesses the uncertainties involved in the projections of seasonal temperature and precipitation changes over South America in the twenty-first century. Climate simulations generated by 24 general circulation models are weighted according to the reliability ensemble averaging (REA) approach. The results show that the REA mean temperature change is slightly smaller over South America compared to the simple ensemble mean. Higher reliability in the temperature projections is found over the La Plata basin, and a larger uncertainty range is located in the Amazon. A temperature increase exceeding 2 °C is found to have a very likely (>90 %) probability of occurrence for the entire South American continent in all seasons, and a more likely than not (>50 %) probability of exceeding 4 °C by the end of this century is found over northwest South America, the Amazon Basin, and Northeast Brazil. For precipitation, the projected changes have the same magnitude as the uncertainty range and are comparable to natural variability.     

Climate projections of future extreme events accounting for modelling uncertainties and historical simulation biases

A methodology is presented for providing projections of absolute future values of extreme weather events that takes into account key uncertainties in predicting future climate. This is achieved by characterising both observed and modelled extremes with a single form of non-stationary extreme value (EV) distribution that depends on global mean temperature and which includes terms that account for model bias. Such a distribution allows the prediction of future “observed” extremes for any period in the twenty-first century. Uncertainty in modelling future climate, arising from a wide range of atmospheric, oceanic, sulphur cycle and carbon cycle processes, is accounted for by using probabilistic distributions of future global temperature and EV parameters. These distributions are generated by Bayesian sampling of emulators with samples weighted by their likelihood with respect to a set of observational constraints. The emulators are trained on a large perturbed parameter ensemble of global simulations of the recent past, and the equilibrium response to doubled CO₂. Emulated global EV parameters are converted to the relevant regional scale through downscaling relationships derived from a smaller perturbed parameter regional climate model ensemble. The simultaneous fitting of the EV model to regional model data and observations allows the characterisation of how observed extremes may change in the future irrespective of biases that may be present in the regional models simulation of the recent past climate. The clearest impact of a parameter perturbation in this ensemble was found to be the depth to which plants can access water. Members with shallow soils tend to be biased hot and dry in summer for the observational period. These biases also appear to have an impact on the potential future response for summer temperatures with some members with shallow soils having increases for extremes that reduce with extreme severity. We apply this methodology for London, using the A1B future emissions scenario to obtain projections of the 50 year return values for the 20 year period centred on 2050. We obtain 10th to 90th percentile ranges of 35.9–42.1 °C for summer daily maximum temperature, 35.5–52.4 mm for summer daily rainfall and 79.2, 97.0 mm for autumn 5 day total rainfall, compared to observed estimates for 1961–1990 of 35.7 °C, 42.1 and 78.4 mm respectively.