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.     

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.     

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.     

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.     

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.     

Global-scale climate impact functions: the relationship between climate forcing and impact

Although there is a strong policy interest in the impacts of climate change corresponding to different degrees of climate change, there is so far little consistent empirical evidence of the relationship between climate forcing and impact. This is because the vast majority of impact assessments use emissions-based scenarios with associated socio-economic assumptions, and it is not feasible to infer impacts at other temperature changes by interpolation. This paper presents an assessment of the global-scale impacts of climate change in 2050 corresponding to defined increases in global mean temperature, using spatially-explicit impacts models representing impacts in the water resources, river flooding, coastal, agriculture, ecosystem and built environment sectors. Pattern-scaling is used to construct climate scenarios associated with specific changes in global mean surface temperature, and a relationship between temperature and sea level used to construct sea level rise scenarios. Climate scenarios are constructed from 21 climate models to give an indication of the uncertainty between forcing and response. The analysis shows that there is considerable uncertainty in the impacts associated with a given increase in global mean temperature, due largely to uncertainty in the projected regional change in precipitation. This has important policy implications. There is evidence for some sectors of a non-linear relationship between global mean temperature change and impact, due to the changing relative importance of temperature and precipitation change. In the socio-economic sectors considered here, the relationships are reasonably consistent between socio-economic scenarios if impacts are expressed in proportional terms, but there can be large differences in absolute terms. There are a number of caveats with the approach, including the use of pattern-scaling to construct scenarios, the use of one impacts model per sector, and the sensitivity of the shape of the relationships between forcing and response to the definition of the impact indicator.     

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     

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.     

Resilience through interlinkage: the green climate fund and climate finance governance

The Green Climate Fund (GCF) is a significant and potentially innovative addition to UNFCCC frameworks for mobilizing increased finance for climate change mitigation and adaptation. Yet the GCF faces challenges of operationalization not only as a relatively new international fund but also as a result of US President Trump’s announcement that the United States would withdraw from the Paris Agreement. Consequently the GCF faces a major reduction in actual funding contributions and also governance challenges at the levels of its Board and the UNFCCC Conference of the Parties (COP), to which it is ultimately accountable. This article analyzes these challenges with reference to the GCF’s internal regulations and its agreements with third parties to demonstrate how exploiting design features of the GCF could strengthen its resilience in the face of such challenges. These features include linkages with UNFCCC constituted bodies, particularly the Technology Mechanism, and enhanced engagement with non-Party stakeholders, especially through its Private Sector Facility. The article posits that deepening GCF interlinkages would increase both the coherence of climate finance governance and the GCF’s ability to contribute to ambitious climate action in uncertain times.

Key policy insights

• The Trump Administration’s purported withdrawal from the Paris Agreement creates challenges for the GCF operating model in three key domains: capitalization, governance and guidance.

• Two emerging innovations could prove crucial in GCF resilience to fulfil its role in Paris Agreement implementation: (1) interlinkages with other UNFCCC bodies, especially the Technology Mechanism; and (2) engagement with non-Party stakeholders, especially private sector actors such as large US investors and financiers.

• There is also an emerging soft role for the GCF as interlocutor between policy-makers and non-Party actors to help bridge the communication divide that often plagues cross-sectoral interactions.

• This role could develop through: (a) the GCF tripartite interface between the Private Sector Facility, Accredited Entities and National Designated Authorities; and (b) strengthened collaborations between the UNFCCC Technical and Financial Mechanisms.

     

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.     

Mapping the climate sceptical blogosphere

While mainstream scientific knowledge production has been extensively examined in the academic literature, comparatively little is known about alternative networks of scientific knowledge production. Online sources such as blogs are an especially under-investigated site of knowledge contestation. Using degree centrality and node betweenness tests from social network analysis, and thematic content analysis of individual posts, this research identifies and critically examines the climate sceptical blogosphere and investigates whether a focus on particular themes contributes to the positioning of the most central blogs. A network of 171 individual blogs is identified, with three blogs in particular found to be the most central: Climate Audit, JoNova and Watts Up With That. These blogs predominantly focus on the scientific element of the climate debate, providing either a direct scientifically-based challenge to mainstream climate science, or a critique of the conduct of the climate science system. This overt scientific framing, as opposed to explicitly highlighting differences in values, politics, or ideological worldview, appears to be an important contributory factor in the positioning of the most central blogs. It is suggested that these central blogs are key protagonists in a process of attempted expert knowledge de-legitimisation and contestation, acting not only as translators between scientific research and lay audiences, but, in their reinterpretation of existing climate science knowledge claims, are acting themselves as alternative public sites of expertise for a climate sceptical audience.     

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.     

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.