Evolution of the Parisian urban climate under a global changing climate

The evolution of the Parisian urban climate under a changing climate is analyzed from long-term offline numerical integrations including a specific urban parameterization. This system is forced by meteorological conditions based on present-climate reanalyses (1970–2007), and climate projections (2071–2099) provided by global climate model simulations following two emission scenarios (A1B and A2). This study aims at quantifying the impact of climate change on air temperature within the city and in the surroundings. A systematic increase of 2-meter air temperature is found. In average according to the two scenarios, it reaches +?2.0/2.4°C in winter and +?3.5/5.0°C in summer for the minimum and maximum daily temperatures, respectively. During summer, the warming trend is more pronounced in the surrounding countryside than in Paris and suburbs due to the soil dryness. As a result, a substantial decrease of the strong urban heat islands is noted at nighttime, and numerous events with negative urban heat islands appear at daytime. Finally, a 30% decrease of the heating degree days is quantified in winter between present and future climates. Inversely, the summertime cooling degree days significantly increase in future climate whereas they are negligible in present climate. However, in terms of accumulated degree days, the increase of the demand in cooling remains smaller than the decrease of the demand in heating.     

An assessment of the surface climate in the NCEP climate forecast system reanalysis

This paper analyzes surface climate variability in the climate forecast system reanalysis (CFSR) recently completed at the National Centers for Environmental Prediction (NCEP). The CFSR represents a new generation of reanalysis effort with first guess from a coupled atmosphere?Cocean?Csea ice?Cland forecast system. This study focuses on the analysis of climate variability for a set of surface variables including precipitation, surface air 2-m temperature (T2m), and surface heat fluxes. None of these quantities are assimilated directly and thus an assessment of their variability provides an independent measure of the accuracy. The CFSR is compared with observational estimates and three previous reanalyses (the NCEP/NCAR reanalysis or R1, the NCEP/DOE reanalysis or R2, and the ERA40 produced by the European Centre for Medium-Range Weather Forecasts). The CFSR has improved time-mean precipitation distribution over various regions compared to the three previous reanalyses, leading to a better representation of freshwater flux (evaporation minus precipitation). For interannual variability, the CFSR shows improved precipitation correlation with observations over the Indian Ocean, Maritime Continent, and western Pacific. The T2m of the CFSR is superior to R1 and R2 with more realistic interannual variability and long-term trend. On the other hand, the CFSR overestimates downward solar radiation flux over the tropical Western Hemisphere warm pool, consistent with a negative cloudiness bias and a positive sea surface temperature bias. Meanwhile, the evaporative latent heat flux in CFSR appears to be larger than other observational estimates over most of the globe. A few deficiencies in the long-term variations are identified in the CFSR. Firstly, dramatic changes are found around 1998?C2001 in the global average of a number of variables, possibly related to the changes in the assimilated satellite observations. Secondly, the use of multiple streams for the CFSR induces spurious jumps in soil moisture between adjacent streams. Thirdly, there is an inconsistency in long-term sea ice extent variations over the Arctic regions between the CFSR and other observations with the CFSR showing smaller sea ice extent before 1997 and larger extent starting in 1997. These deficiencies may have impacts on the application of the CFSR for climate diagnoses and predictions. Relationships between surface heat fluxes and SST tendency and between SST and precipitation are analyzed and compared with observational estimates and other reanalyses. Global mean fields of surface heat and water fluxes together with radiation fluxes at the top of the atmosphere are documented and presented over the entire globe, and for the ocean and land separately.     

Regionalization of the Mediterranean climate

Summary Principal component analysis and cluster analysis applied to annual mean temperature records distributed all over the European Mediterranean countries have allowed the identification of climatic regions.The first three components are significant. They were selected by a dominant variance selection rule, called rule N. The first component corresponds to a fairly uniform field, the second marks the influence of continentality and the third the influence of Africa.A cluster analysis based on the major components shows the existence of four climatic regions. A subjective method, using the zero-line characterizing the significant components, shows similar results.

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Zusammenfassung Komponenten- und Clusteranalysen, angewandt auf Aufzeichnungen der Jahresmitteltemperaturen aus allen europäischen Mittelmeerländern, ermöglichten die Identifizierung von klimatischen Regionen.Die ersten drei Komponenten sind signifikant. Sie wurden mit Hilfe einer Regel, die nach vorherrschenden Varianzen auswählt, der sogenannten N-Regel, bestimmt. Die erste Komponente entspricht einem ziemlich einheitlichen Gebiet, die zweite zeigt den kontinentalen Einfluß und die dritte den Afrikas.Eine Clusteranalyse, die auf den wichtigsten Komponenten basiert, zeigt die Existenz von vier klimatischen Regionen. Eine subjektive Methode zeigt, unter Verwendung einer Nullinie, welche die signifikanten Komponenten kennzeichnet, ähnliche Resultate.

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With 6 Figures     

Framing the way to relate climate extremes to climate change

The atmospheric and ocean environment has changed from human activities in ways that affect storms and extreme climate events. The main way climate change is perceived is through changes in extremes because those are outside the bounds of previous weather. The average anthropogenic climate change effect is not negligible, but nor is it large, although a small shift in the mean can lead to very large percentage changes in extremes. Anthropogenic global warming inherently has decadal time scales and can be readily masked by natural variability on short time scales. To the extent that interactions are linear, even places that feature below normal temperatures are still warmer than they otherwise would be. It is when natural variability and climate change develop in the same direction that records get broken. For instance, the rapid transition from El Ni?o prior to May 2010 to La Ni?a by July 2010 along with global warming contributed to the record high sea surface temperatures in the tropical Indian and Atlantic Oceans and in close proximity to places where record flooding subsequently occurred. A commentary is provided on recent climate extremes. The answer to the oft-asked question of whether an event is caused by climate change is that it is the wrong question. All weather events are affected by climate change because the environment in which they occur is warmer and moister than it used to be.     

On the added value of regional climate modeling in climate change assessment

Regional climate models represent a promising tool to assess the regional dimension of future climate change and are widely used in climate impact research. While the added value of regional climate models has been highlighted with respect to a better representation of land-surface interactions and atmospheric processes, it is still unclear whether radiative heating implies predictability down to the typical scale of a regional climate model. As a quantitative assessment, we apply an optimal statistical filter to compare the coherence between observed and simulated patterns of Mediterranean climate change from a global and a regional climate model. It is found that the regional climate model has indeed an added value in the detection of regional climate change, contrary to former assumptions. The optimal filter may also serve as a weighting factor in multi-model averaging.     

Elevation gradients of European climate change in the regional climate model COSMO-CLM

A transient climate scenario experiment of the regional climate model COSMO-CLM is analyzed to assess the elevation dependency of 21st century European climate change. A focus is put on near-surface conditions. Model evaluation reveals that COSMO-CLM is able to approximately reproduce the observed altitudinal variation of 2 m temperature and precipitation in most regions and most seasons. The analysis of climate change signals suggests that 21st century climate change might considerably depend on elevation. Over most parts of Europe and in most seasons, near-surface warming significantly increases with elevation. This is consistent with the simulated changes of the free-tropospheric air temperature, but can only be fully explained by taking into account regional-scale processes involving the land surface. In winter and spring, the anomalous high-elevation warming is typically connected to a decrease in the number of snow days and the snow-albedo feedback. Further factors are changes in cloud cover and soil moisture and the proximity of low-elevation regions to the sea. The amplified warming at high elevations becomes apparent during the first half of the 21st century and results in a general decrease of near-surface lapse rates. It does not imply an early detection potential of large-scale temperature changes. For precipitation, only few consistent signals arise. In many regions precipitation changes show a pronounced elevation dependency but the details strongly depend on the season and the region under consideration. There is a tendency towards a larger relative decrease of summer precipitation at low elevations, but there are exceptions to this as well.     

Modeling the earth's climate

Mathematical models of the earth's climate provide intriguing opportunities to study a wide range of interdisciplinary problems involving processes within the climate system in a controlled and systematic manner. This paper is intended as a nontechnical review of climate modeling to enable researchers who are unfamiliar with the topic to better evaluate and judge the credibility of the model results. The types of climate models available for climate research are reviewed here, and four broad categories of climate models are identified. These range from the more simple energy balance models (EBMs) and radiative-convective models (RCMs), to the more complex statistical-dynamical models (SDMs), to the most powerful tools yet available for studying climate, the general circulation models (GCMs). This last category includes gridpoint and spectral GCMs. Four representations of the oceans which can be coupled to GCMs are described and include prescribed sea surface temperatures, an energy balance or swamp ocean, a mixed layer or slab ocean, or a fully computed ocean general circulation model. Selected examples considered representative of the types of studies possible with the various classes of models are given. Taken together, the spectrum of climate models provides a hierarchy of learning and research tools with which to effectively study the extremes of past climates, the vagaries of present-day climate, and possible climatic fluctuations well into the future.The National Center for Atmospheric Research is sponsored by the National Science Foundation.     

Quantitative assessment of the climate components driving the pacific decadal oscillation in climate models

The Pacific decadal oscillation (PDO) is defined as the first empirical orthogonal function (EOF) mode of the North Pacific sea surface temperature anomalies. In this study, we reconstructed the PDO using the first-order autoregressive model from various climate indices representing the El Niño-Southern oscillation (ENSO), Aleutian Low (AL), sea surface height (SSH), and thermocline depth over the Kuroshio–Oyashio extension (KOE) region. The climate indices were obtained from observation and twentieth-century simulations of the eight coupled general circulation models (CGCMs) participating in the Climate Model Intercomparison Project Phase III (CMIP3). In this manner, we quantitatively assessed the major climate components generating the PDO using observation and models. Based on observations, the PDO pattern in the central to eastern North Pacific was accurately reconstructed by the AL and ENSO indices, and that in the western North Pacific was best reconstructed by the SSH and thermocline indices. In the CMIP3 CGCMs, the relative contribution of each component to the generation of the PDO varied greatly from model to model, and observations, although the PDO patterns from most of the models were similar to the pattern observed. In the models, the PDO pattern in the eastern and western North Pacific were well reconstructed using the AL and SSH indices, respectively. However, the PDO pattern reconstructed by the ENSO index was quite different from the observed pattern, which was possibly due to the model's common deficiency in simulating the amplitude and location of the ENSO. Furthermore, the differences in the contribution of the KOE thermocline index between the observed pattern and most of the models indicated that the PDO pattern associated with ocean wave dynamics is not properly simulated by most models. Therefore, the virtually well simulated PDO pattern by models is a result of physically inconsistent processes.     

Temporal change of climate zones in China in the context of climate warming

In China, ten climate types were classified using the K-means cluster analysis based on monthly temperature and precipitation data from 753 national meteorological stations for the period 1966–2005. However, 11 mountain climate stations, which are located in southeast China, were classified as one type due to their distinct climate characteristic that differentiated them from other stations. This type could not represent the climate characteristic of this region because all climate stations in this type were located at high-elevation mountains. Thus, it was eliminated when defining climate zones based on climate types. Therefore, nine climate zones were defined in China. Moreover, the temporal change of climate zones was detected in 20-year intervals (1966–1985 and 1986–2005). Although 48 stations changed their climate zones between these two periods, the whole pattern of all climate zones remained stable in these two periods. However, the boundaries between some climate zones changed slightly due to inconsistent variation of regional temperature and precipitation. The most obvious change was the eastern movement of the boundary between an arid temperate zone and a sub-humid temperate zone. There was also a northern shift of the boundary between a tropic zone and a southern subtropic zone. All these changes were probably connected with the climate change in recent 40 years.     

Present climate and climate change over North America as simulated by the fifth-generation Canadian regional climate model

The fifth-generation Canadian Regional Climate Model (CRCM5) was used to dynamically downscale two Coupled Global Climate Model (CGCM) simulations of the transient climate change for the period 1950–2100, over North America, following the CORDEX protocol. The CRCM5 was driven by data from the CanESM2 and MPI-ESM-LR CGCM simulations, based on the historical (1850–2005) and future (2006–2100) RCP4.5 radiative forcing scenario. The results show that the CRCM5 simulations reproduce relatively well the current-climate North American regional climatic features, such as the temperature and precipitation multiannual means, annual cycles and temporal variability at daily scale. A cold bias was noted during the winter season over western and southern portions of the continent. CRCM5-simulated precipitation accumulations at daily temporal scale are much more realistic when compared with its driving CGCM simulations, especially in summer when small-scale driven convective precipitation has a large contribution over land. The CRCM5 climate projections imply a general warming over the continent in the 21st century, especially over the northern regions in winter. The winter warming is mostly contributed by the lower percentiles of daily temperatures, implying a reduction in the frequency and intensity of cold waves. A precipitation decrease is projected over Central America and an increase over the rest of the continent. For the average precipitation change in summer however there is little consensus between the simulations. Some of these differences can be attributed to the uncertainties in CGCM-projected changes in the position and strength of the Pacific Ocean subtropical high pressure.     

Sensitivity of tropical climate to low-level clouds in the NCEP climate forecast system

In this work, we examine the sensitivity of tropical mean climate and seasonal cycle to low clouds and cloud liquid water path (CLWP) by prescribing them in the NCEP climate forecast system (CFS). It is found that the change of low cloud cover alone has a minor influence on the amount of net shortwave radiation reaching the surface and on the warm biases in the southeastern Atlantic. In experiments where CLWP is prescribed using observations, the mean climate in the tropics is improved significantly, implying that shortwave radiation absorption by CLWP is mainly responsible for reducing the excessive surface net shortwave radiation over the southern oceans in the CFS. Corresponding to large CLWP values in the southeastern oceans, the model generates large low cloud amounts. That results in a reduction of net shortwave radiation at the ocean surface and the warm biases in the sea surface temperature in the southeastern oceans. Meanwhile, the cold tongue and associated surface wind stress in the eastern oceans become stronger and more realistic. As a consequence of the overall improvement of the tropical mean climate, the seasonal cycle in the tropical Atlantic is also improved. Based on the results from these sensitivity experiments, we propose a model bias correction approach, in which CLWP is prescribed only in the southeastern Atlantic by using observed annual mean climatology of CLWP. It is shown that the warm biases in the southeastern Atlantic are largely eliminated, and the seasonal cycle in the tropical Atlantic Ocean is significantly improved. Prescribing CLWP in the CFS is then an effective interim technique to reduce model biases and to improve the simulation of seasonal cycle in the tropics.     

湖南气候对全球气候变化的响应

利用湖南省96个台站1960—2010年逐日气象观测资料,在进行均一性检验和订正的基础上对湖南省气候变化事实进行检测分析。结果表明:湖南气候与全球气候变化一致,呈现以变暖为主要特征的变化,且变暖存在季节、地域上的差异,冬、春、秋气温变暖趋势显著,增暖幅度最大的区域在湘北地区;对气候变暖响应敏感的要素主要是与平均气温、冬季气温相关密切的要素,如季平均气温、年平均最低气温、活动积温等;湖南气温在突变时间上具有较好的时间逻辑关系;湖南降水量无显著趋势变化,但极端降水增加,地域性差异明显,湖南东部地区降水量呈现明显增加趋势,日降水量大于等于100 mm的日数呈显著增加趋势;湖南日照时数、风速、相对湿度均呈现显著减少的变化趋势。     

The influence of human activity in the Arctic on climate and climate impacts

Human activities in the Arctic are often mentioned as recipients of climate-change impacts. In this paper we consider the more complicated but more likely possibility that human activities themselves can interact with climate or environmental change in ways that either mitigate or exacerbate the human impacts. Although human activities in the Arctic are generally assumed to be modest, our analysis suggests that those activities may have larger influences on the arctic system than previously thought. Moreover, human influences could increase substantially in the near future. First, we illustrate how past human activities in the Arctic have combined with climatic variations to alter biophysical systems upon which fisheries and livestock depend. Second, we describe how current and future human activities could precipitate or affect the timing of major transitions in the arctic system. Past and future analyses both point to ways in which human activities in the Arctic can substantially influence the trajectory of arctic system change.     

Assessing the influence of climate model uncertainty on EU-wide climate change impact indicators

Despite an increasing understanding of potential climate change impacts in Europe, the associated uncertainties remain a key challenge. In many impact studies, the assessment of uncertainties is underemphasised, or is not performed quantitatively. A key source of uncertainty is the variability of climate change projections across different regional climate models (RCMs) forced by different global circulation models (GCMs). This study builds upon an indicator-based NUTS-2 level assessment that quantified potential changes for three climate-related hazards: heat stress, river flood risk, and forest fire risk, based on five GCM/RCM combinations, and non-climatic factors. First, a sensitivity analysis is performed to determine the fractional contribution of each single input factor to the spatial variance of the hazard indicators, followed by an evaluation of uncertainties in terms of spread in hazard indicator values due to inter-model climate variability, with respect to (changes in) impacts for the period 2041–70. The results show that different GCM/RCM combinations lead to substantially varying impact indicators across all three hazards. Furthermore, a strong influence of inter-model variability on the spatial patterns of uncertainties is revealed. For instance, for river flood risk, uncertainties appear to be particularly high in the Mediterranean, whereas model agreement is higher for central Europe. The findings allow for a hazard-specific identification of areas with low vs. high model agreement (and thus confidence of projected impacts) within Europe, which is of key importance for decision makers when prioritising adaptation options.     

On the ability of the regional climate model RIEMS to simulate the present climate over Asia

A continuous 10-year simulation in Asia for the period of 1 July 1988 to 31 December 1998 was conducted using the Regional Integrated Environmental Model System (RIEMS) with NCEP Reanalysis II data as the driving fields. The model processes include surface physics state package (BATS 1e), a Holtslag explicit planetary boundary layer formulation, a Grell cumulus parameterization, and a modified radiation package (CCM3). Model-produced surface temperature and precipitation are compared with observations from 1001 meteorology stations distributed over Asia and with the 0.5 × 0.5 CRU gridded dataset. The analysis results show that: (1) RIEMS reproduces well the spatial pattern and the seasonal cycle of surface temperature and precipitation; (2) When regionally averaged, the seasonal mean temperature biases are within 1–2C. For precipitation, the model tends to give better simulation in winter than in summer, and seasonal precipitation biases are mostly in the range of ?12%–50%; (3) Spatial correlation coefficients between observed and simulated seasonal precipitation are higher in north of the Yangtze River than in the south and higher in winter than in summer; (4) RIEMS can well reproduce the spatial pattern of seasonal mean sea level pressure. In winter, the model-simulated Siberian high is stronger than the observed. In summer, the simulated subtropical high is shifted northwestwards; (5) The temporal evolution of the East Asia summer monsoon rain belt, with steady phases separated by more rapid transitions, is reproduced.     

Discursive stability meets climate instability: A critical exploration of the concept of ‘climate stabilization’ in contemporary climate policy

The goals and objectives of ‘climate stabilization’ feature heavily in contemporary environmental policy and in this paper we trace the factors that have contributed to the rise of this concept and the scientific ideas behind it. In particular, we explore how the stabilization-based discourse has become dominant through developments in climate science, environmental economics and policymaking. That this discourse is tethered to contemporary policy proposals is unsurprising; but that it has remained relatively free of critical scrutiny can be associated with fears of unsettling often-tenuous political processes taking place at multiple scales. Nonetheless, we posit that the fundamental premises behind stabilization targets are badly matched to the actual problem of the intergenerational management of climate change, scientifically and politically, and destined to fail. By extension, we argue that policy proposals for climate stabilization are problematic, infeasible, and hence impede more productive policy action on climate change. There are gains associated with an expansion and reconsideration of the range of possible policy framings of the problem, which are likely to help us to more capably and dynamically achieve goals of decarbonizing and modernizing the energy system, as well as diminishing anthropogenic contributions to climate change.