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China has warmed rapidly over the past half century and has experienced widespread concomitant impacts on water availability, agriculture and ecosystems. Although urban areas occupy less than 1% of China’s land mass, the majority of China’s observing stations are situated in proximity to urban areas, and thus some of the recorded warming is undoubtedly the consequence of rapid urban development, particularly since the late 1970s. Here, we quantify the separate contributions of urbanization and other external forcings to the observed warming. We estimate that China’s temperature increased by 1.44 °C (90% confidence interval 1.22–1.66 °C) over the period 1961–2013 and that urban warming influences account for about a third of this observed warming, 0.49 °C (0.12–0.86 °C). Anthropogenic and natural external forcings combined explain most of the rest of the observed warming, contributing 0.93 °C (0.61–1.24 °C). This is close to the warming of 1.09 °C (0.86–1.31 °C) observed in global mean land temperatures over the period 1951–2010, which, in contrast to China’s recorded temperature change, is only weakly affected by urban warming influences. Clearly the effects of urbanization have considerably exacerbated the warming experienced by the large majority of the Chinese population in comparison with the warming that they would have experienced as a result of external forcing alone.

Extreme weather and climate-related events occur in a particular place, by definition, infrequently. It is therefore challenging to detect systematic changes in their occurrence given the relative shortness of observational records. However, there is a clear interest from outside the climate science community in the extent to which recent damaging extreme events can be linked to human-induced climate change or natural climate variability. Event attribution studies seek to determine to what extent anthropogenic climate change has altered the probability or magnitude of particular events. They have shown clear evidence for human influence having increased the probability of many extremely warm seasonal temperatures and reduced the probability of extremely cold seasonal temperatures in many parts of the world. The evidence for human influence on the probability of extreme precipitation events, droughts, and storms is more mixed. Although the science of event attribution has developed rapidly in recent years, geographical coverage of events remains patchy and based on the interests and capabilities of individual research groups. The development of operational event attribution would allow a more timely and methodical production of attribution assessments than currently obtained on an ad hoc basis. For event attribution assessments to be most useful, remaining scientific uncertainties need to be robustly assessed and the results clearly communicated. This requires the continuing development of methodologies to assess the reliability of event attribution results and further work to understand the potential utility of event attribution for stakeholder groups and decision makers.

A theoretically expected consequence of the intensification of the hydrological cycle under global warming is that on average, wet regions get wetter and dry regions get drier (WWDD). Recent studies, however, have found significant discrepancies between the expected pattern of change and observed changes over land. We assess the WWDD theory in four climate models. We find that the reported discrepancy can be traced to two main issues: (1) unforced internal climate variability strongly affects local wetness and dryness trends and can obscure underlying agreement with WWDD, and (2) dry land regions are not constrained to become drier by enhanced moisture divergence since evaporation cannot exceed precipitation over multiannual time scales. Over land, where the available water does not limit evaporation, a “wet gets wetter” signal predominates. On seasonal time scales, where evaporation can exceed precipitation, trends in wet season becoming wetter and dry season becoming drier are also found.

Land-atmosphere interactions play an important role for hot temperature extremes in Europe. Dry soils may amplify such extremes through feedbacks with evapotranspiration. While previous observational studies generally focused on the relationship between precipitation deficits and the number of hot days, we investigate here the influence of soil moisture (SM) on summer monthly maximum temperatures (TXx) using water balance model-based SM estimates (driven with observations) and temperature observations. Generalized extreme value distributions are fitted to TXx using SM as a covariate. We identify a negative relationship between SM and TXx, whereby a 100 mm decrease in model-based SM is associated with a 1.6 °C increase in TXx in Southern-Central and Southeastern Europe. Dry SM conditions result in a 2–4 °C increase in the 20-year return value of TXx compared to wet conditions in these two regions. In contrast with SM impacts on the number of hot days (NHD), where low and high surface-moisture conditions lead to different variability, we find a mostly linear dependency of the 20-year return value on surface-moisture conditions. We attribute this difference to the non-linear relationship between TXx and NHD that stems from the threshold-based calculation of NHD. Furthermore the employed SM data and the Standardized Precipitation Index (SPI) are only weakly correlated in the investigated regions, highlighting the importance of evapotranspiration and runoff for resulting SM. Finally, in a case study for the hot 2003 summer we illustrate that if 2003 spring conditions in Southern-Central Europe had been as dry as in the more recent 2011 event, temperature extremes in summer would have been higher by about 1 °C, further enhancing the already extreme conditions which prevailed in that year.

Establishing the precise timing for the onset of ice-sheet retreat at the end of the Last Glacial Maximum (LGM) is critical for delineating mechanisms that drive deglaciations. Uncertainties in the timing of ice-margin retreat and global ice-volume change allow a variety of plausible deglaciation triggers. Using boulder 10Be surface exposure ages, we date initial southern Laurentide ice-sheet (LIS) retreat from LGM moraines in Wisconsin (USA) to 23.0 ± 0.6 ka, coincident with retreat elsewhere along the southern LIS and synchronous with the initial rise in boreal summer insolation 24–23 ka. We show with climate-surface mass balance simulations that this small increase in boreal summer insolation alone is potentially sufficient to drive enhanced southern LIS surface ablation. We also date increased southern LIS retreat after ca. 20.5 ka likely driven by an acceleration in rising isolation. This near-instantaneous southern LIS response to boreal summer insolation before any rise in atmospheric CO2 supports the Milanković hypothesis of orbital forcing of deglaciations.

The Climate Symposium 2014, organized by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and the World Climate Research Programme (WCRP), was entitled “Climate Research and Earth Observation from Space—Climate Information for Decision Making.” Session topics revolved around the six Grand Science Challenges of the WCRP and addressed the specific need for, and role of, climate observations from space. Based on the presentations and discussions at the symposium, the Science Programme Committee identified main findings and recommendations, which are presented in this summary.

Quantile mapping bias correction algorithms are commonly used to correct systematic distributional biases in precipitation outputs from climate models. Although they are effective at removing historical biases relative to observations, it has been found that quantile mapping can artificially corrupt future model-projected trends. Previous studies on the modification of precipitation trends by quantile mapping have focused on mean quantities, with less attention paid to extremes. This article investigates the extent to which quantile mapping algorithms modify global climate model (GCM) trends in mean precipitation and precipitation extremes indices. First, a bias correction algorithm, quantile delta mapping (QDM), that explicitly preserves relative changes in precipitation quantiles is presented. QDM is compared on synthetic data with detrended quantile mapping (DQM), which is designed to preserve trends in the mean, and with standard quantile mapping (QM). Next, methods are applied to phase 5 of the Coupled Model Intercomparison Project (CMIP5) daily precipitation projections over Canada. Performance is assessed based on precipitation extremes indices and results from a generalized extreme value analysis applied to annual precipitation maxima. QM can inflate the magnitude of relative trends in precipitation extremes with respect to the raw GCM, often substantially, as compared to DQM and especially QDM. The degree of corruption in the GCM trends by QM is particularly large for changes in long period return values. By the 2080s, relative changes in excess of +500% with respect to historical conditions are noted at some locations for 20-yr return values, with maximum changes by DQM and QDM nearing +240% and +140%, respectively, whereas raw GCM changes are never projected to exceed +120%.

Recent improvements in forecast skill of the climate system by dynamical climate models could lead to improvements in seasonal streamflow predictions. This study evaluates the hydrologic prediction skill of a dynamical climate model–driven hydrologic prediction system (CM-HPS), based on an ensemble of statistically downscaled outputs from the Canadian Seasonal to Interannual Prediction System (CanSIPS). For comparison, historical and future climate traces–driven ensemble streamflow prediction (ESP) was employed. The Variable Infiltration Capacity model (VIC) hydrologic model setup for the Fraser River basin, British Columbia, Canada, was used as a test bed for the two systems. In both cases, results revealed limited precipitation prediction skill. For streamflow prediction, the ESP approach has very limited or no correlation skill beyond the months influenced by initial hydrologic conditions, while the CM-HPS has moderately better correlation skill, attributable to the enhanced temperature prediction skill that results from CanSIPS’s ability to predict El Niño–Southern Oscillation (ENSO) and its teleconnections. The root-mean-square error, bias, and categorical skills for the two methods are mostly similar. Hydrologic modeling uncertainty also affects the prediction skill, and in some cases prediction skill is constrained by hydrologic model skill. Overall, the CM-HPS shows potential for seasonal streamflow prediction, and further enhancements in climate models could potentially to lead to more skillful hydrologic predictions.

Various hydrologic models with different complexities have been developed to represent the characteristics of river basins, improve streamflow forecasts such as seasonal volumetric flow predictions, and meet other demands from different stakeholders. Because no single hydrologic model is able to perfectly simulate the observed flow, multimodel combination techniques are developed to combine forecasts obtained from different models and to quantify the uncertainties with the goal of improving upon single-model performance. In this study, a comprehensive set of multimodel ensemble averaging techniques with varying complexities are investigated for operational forecasting over four river basins in the Western United States. Ensemble merging models are divided into three categories of simple, intermediate, and complex, and comparison is made between each class by using a bootstrap approach. Analysis suggests that model combination effectively improves most of the individual seasonal forecasts and can outperform the best forecast model. Simple average, median, Bates-Granger, constrained linear regression, and Bayesian model averaging optimized by expectation maximization showed better results compared with other methods over three basins. For the Rogue River basin, the intermediate and complex models outperformed most of the individual forecasts and the simple methods. Multimodeling techniques based on information criteria showed similar performances.