Regional climate modelling over complex terrain: an evaluation study of COSMO-CLM hindcast model runs for the Greater Alpine Region

In this study the results of the regional climate model COSMO-CLM (CCLM) covering the Greater Alpine Region (GAR, 4°–19°W and 43°–49°N) were evaluated against observational data. The simulation was carried out as a hindcast run driven by ERA-40 reanalysis data for the period 1961–2000. The spatial resolution of the model data presented is approx. 10 km per grid point. For the evaluation purposes a variety of observational datasets were used: CRU TS 2.1, E-OBS, GPCC4 and HISTALP. Simple statistics such as mean biases, correlations, trends and annual cycles of temperature and precipitation for different sub-regions were applied to verify the model performance. Furthermore, the altitude dependence of these statistical measures has been taken into account. Compared to the CRU and E-OBS datasets CCLM shows an annual mean cold bias of ?0.6 and ?0.7 °C, respectively. Seasonal precipitation sums are generally overestimated by +8 to +23 % depending on the observational dataset with large variations in space and season. Bias and correlation show a dependency on altitude especially in the winter and summer seasons. Temperature trends in CCLM contradict the signals from observations, showing negative trends in summer and autumn which are in contrast to CRU and E-OBS.     

Projections of daily mean temperature variability in the future: cross-validation tests with ENSEMBLES regional climate simulations

Because of model biases, projections of future climate need to combine model simulations of recent and future climate with information on observed climate. Here, 10 methods for projecting the distribution of daily mean temperatures are compared, using six regional climate change simulations for Europe. Cross validation between the models is used to assess the potential performance of the methods in projecting future climate. Delta change and bias correction type methods show similar cross-validation performance, with methods based on the quantile mapping approach doing best in both groups due to their apparent ability to reduce the errors in the projected time mean temperature change. However, as no single method performs best under all circumstances, the optimal approach might be to use several well-behaving methods in parallel. When applying the various methods to real-world temperature projection for the late 21st century, the largest intermethod differences are found in the tails of the temperature distribution. Although the intermethod variation of the projections is generally smaller than their intermodel variation, it is not negligible. Therefore, it should be preferably included in uncertainty analysis of temperature projections, particularly in applications where the extremes of the distribution are important.     

Statistical modeling of extreme value behavior in North American tree-ring density series

Many analyses of the paleoclimate record include conclusions about extremes, with a focus on the unprecedented nature of recent climate events. While the use of extreme value theory is becoming common in the analysis of the instrumental climate record, applications of this framework to the spatio-temporal analysis of paleoclimate records remain limited. This article develops a Bayesian hierarchical model to investigate spatially varying trends and dependencies in the parameters characterizing the distribution of extremes of a proxy data set, and applies it to the site-wise decadal maxima and minima of a gridded network of temperature sensitive tree ring density time series over northern North America. The statistical analysis reveals significant spatial associations in the temporal trends of the location parameters of the generalized extreme value distributions: maxima are increasing as a function of time, with stronger increases in the north and east of North America; minima are significantly increasing in the west, possibly decreasing in the east, and exhibit no changes in the center of the region. Results indicate that the distribution varies as a function of both space and time, with tree ring density maxima becoming more extreme as a function of time and minima having diverging temporal trends, by spatial location. Results of this proxy-only analysis are a first step towards directly reconstructing extremal climate behavior, as opposed to mean climate behavior, by linking extremes in the proxy record to extremes in the instrumental record.     

Climate change and infectious diseases: Can we meet the needs for better prediction?

The next generation of climate-driven, disease prediction models will most likely require a mechanistically based, dynamical framework that parameterizes key processes at a variety of locations. Over the next two decades, consensus climate predictions make it possible to produce forecasts for a number of important infectious diseases that are largely independent of the uncertainty of longer-term emissions scenarios. In particular, the role of climate in the modulation of seasonal disease transmission needs to be unravelled from the complex dynamics resulting from the interaction of transmission with herd immunity and intervention measures that depend upon previous burdens of infection. Progress is also needed to solve the mismatch between climate projections and disease projections at the scale of public health interventions. In the time horizon of seasons to years, early warning systems should benefit from current developments on multi-model ensemble climate prediction systems, particularly in areas where high skill levels of climate models coincide with regions where large epidemics take place. A better understanding of the role of climate extremes on infectious diseases is urgently needed.     

Analysis of the surface temperature and wind forecast errors of the NCAR-AirDat operational CONUS 4-km WRF forecasting system

Investigating the characteristics of model-forecast errors using various statistical and object-oriented methods is necessary for providing useful guidance to end-users and model developers as well. To this end, the random and systematic errors (i.e., biases) of the 2-m temperature and 10-m wind predictions of the NCAR-AirDat weather research and forecasting (WRF)-based real-time four-dimensional data assimilation (RTFDDA) and forecasting system are analyzed. This system has been running operationally over a contiguous United States (CONUS) domain at a 4-km grid spacing with four forecast cycles daily from June 2009 to September 2010. In the result an exceptionally useful forecast dataset was generated and used for studying the error properties of the model forecasts, in terms of both a longer time period and a broader coverage of geographic regions than previously studied. Spatiotemporal characteristics of the errors are investigated based on the 24-h forecasts between June 2009 and April 2010, and the 72-h forecasts between May and September 2010. It was found that the biases of both wind and temperature forecasts vary greatly seasonally and diurnally, with dependency on the forecast length, station elevation, geographical location, and meteorological conditions. The temperature showed systematic cold biases during the daytime at all station elevations and warm biases during the nighttime above 1,000 m above sea level (ASL), while below 600 m ASL cold biases occurred during the nighttime. The forecasts of surface wind speed exhibited strong positive biases during the nighttime, while the negative biases were observed in the spring and summer afternoons. The surface wind speed was mostly over-predicted except for the stations located between 1,000 and 2,100 m ASL, for which negative biases were identified for most forecast cycles. The highest wind-speed errors were found over the high terrain and near sea-level stations. The wind-direction errors were relatively large at the high-terrain elevation in the Rocky and Appalachian mountain ranges and the western coastal areas and the error structure exhibited notable diurnal variability.     

Circulation over Bulgaria and its connection with NAO indices and Sea Surface Temperatures

Trends in atmospheric pressure, circulation and some relationships between North Atlantic Oscillation (NAO) indices, sea surface temperatures, and atmospheric circulation over Bulgaria are discussed in this article. Data for measured atmospheric pressure at stations Burgas, Pleven, and Sandanski are used. Information about atmospheric circulation over Bulgaria was obtained using sea level pressure and 700 hPa Omega (vertical velocity) reanalysis daily data for grid cells covering the territory of Bulgaria for the period 1948–2010. Zonal and meridional indices for Bulgaria were also calculated based on the data for sea level pressure. NAO index calculated by NOAA and NCAR is correlated with atmospheric pressure and circulation. A total of 12 areas in three major water basins influencing Bulgarian climate—North Atlantic, Mediterranean, and Black Seas—were studied. Main methods employed in the article are statistical—trend analysis, multiple linear regression, correlation, nonparametric tests, etc. There is no change in the mean values of atmospheric pressure over Bulgaria. Circulation over Bulgaria during the research period increases its anticyclonal patterns mainly due to the decrease of the number of cyclones. Dynamics in zonal and meridional indices for Bulgaria result in an increase of the northwest transport in the winter and an increase of the northeast transport in the summer. Cyclones over Bulgaria determine the values of atmospheric pressure. Influence of the NAO on atmospheric pressure and circulation is stronger in winter. Atmospheric processes, expressed by the number of cyclones and anticyclones, are most active in spring. Current trends are towards increasing of sea surface temperatures (SSTs) at all investigated places. Temporally, the effect of SSTs on the number of cyclones, anticyclones, zonal and meridional indices for Bulgaria during the different seasons comes with a delay of 1 to 3 months. Constructed multiple linear regression (MLR) models with predictors SSTs adequately describe the atmospheric circulation over Bulgaria. There is a clear pattern of SSTs distribution, which leads to a higher number of cyclones over Bulgaria in winter—lower than normal temperatures in the Aegean Sea and higher than normal in the Black Sea. A decrease in the difference of temperatures between the Gulf Stream and western colder parts leads to higher values of winter zonal transport over Bulgaria. Higher than normal temperatures in Black Sea lead to a higher number of cyclones in spring. Higher difference in temperatures of the North Atlantic leads to a stronger cyclogenesis and enhanced zonal transport, which affects autumn circulation over Bulgaria.     

US institutional pathways to clean coal and shale gas: lessons for China

China's 12th Five-Year Plan (2011–2015) envisages that shale gas and coal will be central to its energy future. However, for China to meet the energy security and climate change objectives set out in its 12th Five-Year Plan it will be reliant on the widespread commercial deployment of two key technologies; hydraulic fracturing combined with horizontal drilling for shale gas, and carbon capture and storage (CCS) for coal. China is moving to acquire these technologies through technology transfer and diffusion from the US, but progress has been slow, and neither is currently available in China on a commercial scale. Drawing on interviews in the US and China, this article argues that China's expectation of technology from the US may well be disappointed because of factors unique to the US institutional environment that have made the development of fracking technology possible and hinder the development of CCS technology at a commercial scale.

Policy relevance

If China is to meet the energy security and climate change objectives set out in its 12th Five-Year Plan it will be reliant on the widespread commercial deployment of fracking and clean coal technologies. While China expects to acquire these technologies via technology transfer and diffusion from the US, progress has been slow. Because of factors unique to the US institutional environment the availability of both technologies on a commercial scale in China is unlikely in the coming years. As a result, Chinese policy makers would be well-advised not to count on these technologies to meet their energy and climate goals.     

Landscape for change? International climate policy and energy transitions: evidence from sub-Saharan Africa

What is the role of the climate regime in facilitating rapid decarbonization of the world’s energy systems? We examine how core assumptions concerning the roles of the nation state, carbon markets and finance and technology in international climate policy are being challenged by the realities of how transitions in the energy systems are unfolding. Drawing on the critical region of sub-Saharan Africa, we examine the potential for international climate policy to foster new trajectories towards decarbonization.

Policy relevance

The international regime for climate policy has been in place for some twenty years. Despite significant changes in the landscape of energy systems and drivers of global GHG emissions over this time, the core principles and tools remain relatively stable – national governments, carbon markets, project-based climate finance and the transfer of technological hardware. Given the diversity of actors and drivers and the limited direct reach and influence of international climate policy, however, there is an urgent need to consider how the climate regime can best support the embryonic transitions that are slowly taking form around the world. To do this effectively requires a more nuanced understanding of the role of the state in governing these transitions beyond the notion of a cohesive state serving as rule-enforcer and transition manager. It also requires a broader view of technology, not just as hardware that is transferred, but as a set of practices and networks of expertise and enabling actors. Likewise, though markets have an important role to play as vehicles for achieving broader ends, they are not an end in themselves. Finally on finance, while acknowledging the important role of climate aid, often as a multiplier or facilitator of more ambitious private flows, it is critical to differentiate between the types of finance required for different transitions, many of which will not be counted under, or directed by, the climate regime. In sum, the (low-) carbon economy is being built in ways and in numerous sites that the climate regime needs to be cognizant of and engage with productively, and this may require fundamental reconsideration of the building blocks of the international climate regime.     

Cross-national patterns of governance mechanisms in nationally determined contributions (NDCs) under the Paris Agreement

ABSTRACT

The continuous submission and scaling-up of Nationally Determined Contributions (NDCs) constitutes a key feature of the Paris Agreement. In their NDCs, states propose governance mechanisms for implementation of climate action, in turn distinguishing appropriate roles for the state in climate governance. Clarity on Parties’ suggested roles for the state makes explicit assumptions on the premise of climate policy, in turn contributing to enhanced transparency in negotiations on the scaling-up of NDCs. This also speaks to ongoing debates on roles for the state in climate governance literature. This article identifies the governance mechanisms proposed by states in their NDCs and the roles for the state envisioned by those governance mechanisms, and also examines how cross-national patterns of roles for the state break or converge with conventional patterns of international politics. The analysis shows that states propose a plurality of roles, which to different extents may be complementary or conflictual. We conclude that income, region, and the Annexes under the United Nations Framework Convention on Climate Change (UNFCCC) are important for understanding suggested roles for the state, but that there are nuances to be further explored. We argue that this paper has three key findings: i) a majority of states rely on market mechanisms to implement their NDCs while rules on implementation and assessment of market mechanisms are still an outstanding issue in the negotiations, meaning that resolving this issue will be essential; ii) the process for evaluating and assessing qualitative governance mechanisms needs to be specified; and iii) increased awareness of differing views on the state’s roles makes explicit different perspectives on what constitutes an ambitious and legitimate contribution to combating climate change.

Key policy insights

• A majority of states (> 75%) envision the state as regulator (creating and strengthening legislation), market facilitator (creating and maintaining market structures), or facilitator (creating more favourable material conditions for climate-friendly behaviour).

• Greater awareness of differing views on roles for the state can increase understanding of different perspectives on ambition and legitimacy of contributions, in turn facilitating trust in negotiations.

• A distinction between substantive and procedural qualitative governance mechanisms and their function and interaction would facilitate the stocktaking dialogues.

     

The High-Resolution Coronal Imager (Hi-C)

The High-Resolution Coronal Imager (Hi-C) was flown on a NASA sounding rocket on 11 July 2012. The goal of the Hi-C mission was to obtain high-resolution (≈?0.3?–?0.4′′), high-cadence (≈?5 seconds) images of a solar active region to investigate the dynamics of solar coronal structures at small spatial scales. The instrument consists of a normal-incidence telescope with the optics coated with multilayers to reflect a narrow wavelength range around 19.3 nm (including the Fe xii 19.5-nm spectral line) and a 4096×4096 camera with a plate scale of 0.1′′?pixel^?1. The target of the Hi-C rocket flight was Active Region 11520. Hi-C obtained 37 full-frame images and 86 partial-frame images during the rocket flight. Analysis of the Hi-C data indicates the corona is structured on scales smaller than currently resolved by existing satellite missions.