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Gaps with missing data in the observational temperature record are responsible for an underestimation of the global warming between 1881–1910 and 1986–2015 by 0.1 °C. We found that missing data in the historical observations introduce a warm bias in the early part of the record and a cold bias toward the end. The effect of the nonuniform sampling was explored by comparing the global mean temperature estimated from gridded observations, climate model simulations, and reanalysis. Output from global simulations was subsampled by masking the grid boxes corresponding to those with missing data in the observations to mimic the geographical availability of temperature measurements. A combination of variance depending on region and a varying geographical data sampling over time explains the bias in the global mean. We propose a methodology for estimating the global mean temperature that reduces the effect of the nonuniform variance. Key Points: The incomplete sampling of the surface temperature of Earth's surface affects the estimation of trends in the global meanThe changing availability of temperature observations over time influences the variance of the data sampleAn inhomogeneity in the data in terms of changing variance affects the trend estimate for the global mean temperature [ABSTRACT FROM AUTHOR]

VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process‐based, etc.). Here we describe the participating methods and first results from the first experiment, using "perfect" reanalysis (and reanalysis‐driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques. Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method‐to‐method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO‐CORDEX initiative (where VALUE activities have merged and follow on). Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at http://www.value‐cost.eu/data and data and validation results are available from the VALUE validation portal for further investigation: http://www.value‐cost.eu/validationportal. [ABSTRACT FROM AUTHOR]

The application of climate change impact assessment (CCIA) studies in general and especially the linkages between different actor groups typically involved is often not trivial and subject to many limitations and uncertainties. Disciplinary issues like competing downscaling approaches, imperfect climate and impact model data and uncertainty propagation as well as the selection of appropriate data sets are only one part of the story. Interdisciplinary and transdisciplinary challenges add to these, as climate data and impact model data provision and their usage require at least a minimum of common work and shared understanding among actors. Here, we provide the VALUE perspective on current disciplinary challenges and limitations at the downscaling interface and elaborate transdisciplinary issues that hamper a proper working downscaling interface. The perspective is partly based on a survey on user needs of downscaled data that was distributed among 62 participants across Europe involving 22 sectors. Partly, it is based on the exchanges and experiences gained during the lifetime of VALUE that brought together different actor groups of different disciplines: climate modellers, impact modellers, statisticians and stakeholders. We outline a sketch that summarizes the linkages between the main identified actor groups: climate model data providers, impact modellers and societal users. We summarize review and structure current actors groups, needs and issues. We argue that this structuring enables involved actors to tackle these issues in a more organized and hence effective way. A key solution to several difficulties at the downscaling interface is to our understanding the development of guidelines based on benchmark tests like the VALUE framework. In addition, fostering communication between actor groups—and financing this communication—is essential to obtain the best possible CCIA as a prerequisite for robust adaptation. [ABSTRACT FROM AUTHOR]

ABSTRACT A 175 years long homogenized composite record of monthly mean temperatures is presented for Oslo, the capital of Norway. The early raw data have been digitised and quality controlled, and monthly means have been calculated. Some early original observations carried out in a Wild screen (1877-1936) were found to be spuriously high because of inappropriate sheltering from sunlight. These spurious temperatures were not used in the composite record, but alternative temperatures measured (1837-1933) by thermometers placed outside windows at the Astronomical Observatory were used instead. No inhomogeneity was detected in the latter series after adding an instrument correction of +0.3 °C, but the start year of the correction remains uncertain. The more recent part of the composite record used the long-term series (1937 to present) from Blindern in Oslo, the premises of The Norwegian Meteorological Institute. Two small inhomogeneities were detected in the Blindern series, possibly caused by a weak urban heat island effect or growing/cutting of trees. The study revealed that the annual mean temperature has increased by 1.5 °C in the period 1838-2012. The most pronounced increase in annual temperature occurred during the last 50 years, and in the early 20th century that ended with a local maximum in the 1930s. The temperature has increased significantly in all seasons; however, the temperature increase in summer was less than a half of that in winter and spring, which were the seasons with largest increase. In addition the monthly mean temperature of the coldest month in each year has increased two times faster than the warmest one. The most significant temperature variations were associated to ∼ 5-year time scales in its early part, but since 1930 and up to present, the dominant time scales were 10-20 years. [ABSTRACT FROM AUTHOR]

We predicted future plague and black-tailed prairie dog dynamics in the North American prairies under different scenarios of climate change. A climate-driven model for the joint dynamic of the host–parasite system was used. Projections for the regional climate were obtained through empirical–statistical downscaling of global climate scenarios generated by an ensemble of global climate models for the recent Fourth Assessment Report by the IPCC. The study shows the uncertainties involved in predicting future regional climate and climate-driven population dynamics, but reveals that unchanged or lower levels of plague, leading to increased black-tailed prairie dog colonies, can be expected. Less plague is particularly expected for scenarios that assume the highest emission of greenhouse gases associated with the greatest projected future warming. Moreover, under high-emission scenarios, decreased probabilities of extremely high numbers of infected colonies are expected, along with decreased probabilities of extremely low total numbers of colonies. The assumed main underlying mechanism is an inhibiting effect of high temperatures on fleas (dispersal vector) and on flea-mediated transmission of the disease-causing bacterium. Our study highlights the importance of using dynamic ecological (here host–parasite) models together with ensembles of climate projections to investigate the responses of populations and parasites to a changed climate. [ABSTRACT FROM AUTHOR]

The article offers information on the statistical models for downscaling precipitation and their ability to predict long term trends. It is inferred that large-scale climactic changes may not give a precise representation of changes in precipitation as a result of global warming. The statistical models used in studying the atmospheric trend and condition in Scandinavia were capable of capturing data of the year-to-year seasonal changes in precipitation, and predicted the correlation of climactic trends in the region. Details related on the degree of non-stationary in the statistical relationship between large and local scales is presented.