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Abstract Postprocessing of climate model outputs is usually performed to remove biases prior to performing climate change impact studies. The evaluation of the performance of bias correction methods is routinely done by comparing postprocessed outputs to observed data. However, such an approach does not take into account the inherent uncertainty linked to natural climate variability and may end up recommending unnecessary complex postprocessing methods. This study evaluates the performance of bias correction methods using natural variability as a baseline. This baseline implies that any bias between model simulations and observations is only significant if it is larger than the natural climate variability. Four bias correction methods are evaluated with respect to reproducing a set of climatic and hydrological statistics. When using natural variability as a baseline, complex bias correction methods still outperform the simplest ones for precipitation and temperature time series, although the differences are much smaller than in all previous studies. However, after driving a hydrological model using the bias-corrected precipitation and temperature, all bias correction methods perform similarly with respect to reproducing 46 hydrological metrics over two watersheds in different climatic zones. The sophisticated distribution mapping correction methods show little advantage over the simplest scaling method. The main conclusion is that simple bias correction methods appear to be just as good as other more complex methods for hydrological climate change impact studies. While sophisticated methods may appear more theoretically sound, this additional complexity appears to be unjustified in hydrological impact studies when taking into account the uncertainty linked to natural climate variability.

Abstract Increased temperature will result in longer, more frequent, and more intense heat waves. Changes in temperature variability have been deemed necessary to account for future heat wave characteristics. However, this has been quantified only in Europe and North America, while the rest of the globe remains unexplored. Using late century global climate projections, we show that annual mean temperature increases is the key factor defining heat wave changes in most regions. We find that commonly studied areas are an exception rather than the standard and the mean climate change signal generally outweighs any influence from variability changes. More importantly, differences in warming across seasons are responsible for most of the heat wave changes and their consideration relegates the contribution of variability to a marginal role. This reveals that accurately capturing mean seasonal changes is crucial to estimate future heat waves and reframes our interpretation of future temperature extremes. , Key Points The influence of projected temperature variability changes on future heat waves varies across the globe Future heat waves are primarily controlled by annual mean changes, except in Europe and North America Mean seasonal warming is responsible for over 95% of projected heat wave changes in most region

Abstract The summer rainfall across Sahelian‐Sudan is one of the main sources of water for agriculture, human, and animal needs. However, the rainfall is characterized by large interannual variability, which has attracted extensive scientific efforts to understand it. This study attempts to identify the source regions that contribute to the Sahelian‐Sudan moisture budget during July through September. We have used an atmospheric general circulation model with an embedded moisture‐tracing module (Community Atmosphere Model version 3), forced by observed (1979–2013) sea‐surface temperatures. The result suggests that about 40% of the moisture comes with the moisture flow associated with the seasonal migration of the Intertropical Convergence Zone (ITCZ) and originates from Guinea Coast, central Africa, and the Western Sahel. The Mediterranean Sea, Arabian Peninsula, and South Indian Ocean regions account for 10.2%, 8.1%, and 6.4%, respectively. Local evaporation and the rest of the globe supply the region with 20.3% and 13.2%, respectively. We also compared the result from this study to a previous analysis that used the Lagrangian model FLEXPART forced by ERA‐Interim. The two approaches differ when comparing individual regions, but are in better agreement when neighboring regions of similar atmospheric flow features are grouped together. Interannual variability with the rainfall over the region is highly correlated with contributions from regions that are associated with the ITCZ movement, which is in turn linked to the Atlantic Multidecadal Oscillation. Our result is expected to provide insights for the effort on seasonal forecasting of the rainy season over Sahelian Sudan. , Key Points The moisture associated with ITCZ flow accounts for about 40%‐50% of the precipitated water The local evaporation provides about 20% of the precipitated water The multiyear variability in the rainfall seems to be linked to the AMO

Abstract. Leaf area index (LAI) is an important parameter related to carbon, water, and energy exchange between canopy and atmosphere and is widely applied in process models that simulate production and hydrological cycles in forest ecosystems. However, fine-scale spatial heterogeneity of LAI and its controlling factors have yet to be fully understood in Chinese subtropical forests. We used hemispherical photography to measure LAI values in three subtropical forests (Pinus massoniana–Lithocarpus glaber coniferous and evergreen broadleaved mixed forests, Choerospondias axillaris deciduous broadleaved forests, and L. glaber–Cyclobalanopsis glauca evergreen broadleaved forests) from April 2014 to January 2015. Spatial heterogeneity of LAI and its controlling factors were analysed using geostatistical methods and the generalised additive models (GAMs) respectively. Our results showed that LAI values differed greatly in the three forests and their seasonal variations were consistent with plant phenology. LAI values exhibited strong spatial autocorrelation for the three forests measured in January and for the L. glaber–C. glauca forest in April, July, and October. Obvious patch distribution pattern of LAI values occurred in three forests during the non-growing period and this pattern gradually dwindled in the growing season. Stem number, crown coverage, proportion of evergreen conifer species on basal area basis, proportion of deciduous species on basal area basis, and forest types affected the spatial variations in LAI values in January, while stem number and proportion of deciduous species on basal area basis affected the spatial variations in LAI values in July. Floristic composition, spatial heterogeneity, and seasonal variations should be considered for sampling strategy in indirect LAI measurement and application of LAI to simulate functional processes in subtropical forests.

Abstract Moso bamboo can rapidly complete its growth in both height and diameter within only 35–40 days after shoot emergence. However, the underlying mechanism for this “explosive growth” remains poorly understood. We investigated the dynamics of non-structural carbohydrates (NSCs) in shoots and attached mature bamboos over a 20-month period. The results showed that Moso bamboos rapidly completed their height and diameter growth within 38 days. At the same time, attached mature bamboos transferred almost all the NSCs of their leaves, branches and especially trunks and rhizomes to the “explosively growing” shoots via underground rhizomes for the structural growth and metabolism of shoots. Approximately 4 months after shoot emergence, this transfer stopped when the leaves of the young bamboos could independently provide enough photoassimilates to meet the carbon demands of the young bamboos. During this period, the NSC content of the leaves, branches, trunks and rhizomes of mature bamboos declined by 1.5, 23, 28 and 5 fold, respectively. The trunk contributed the most NSCs to the shoots. Our findings provide new insight and a possible rational mechanism explaining the “explosive growth” of Moso bamboo and shed new light on understanding the role of NSCs in the rapid growth of Moso bamboo.

Abstract Reanalyses have the potential to provide meteorological information in areas where few or no traditional observation records are available. The terrestrial branch of the water cycle of CFSR, MERRA, ERA-Interim, and NARR is examined over Quebec, Canada, for the 1979–2008 time period. Precipitation, evaporation, runoff, and water balance are studied using observed precipitation and streamflows, according to three spatial scales: 1) the entire province of Quebec, 2) five regions derived from a climate classification, and 3) 11 river basins. The results reveal that MERRA provides a relatively closed water balance, while a significant residual was found for the other three reanalyses. MERRA and ERA-Interim seem to provide the most reliable precipitation over the province. On the other hand, precipitation from CFSR and NARR do not appear to be particularly reliable, especially over southern Quebec, as they almost systematically showed the highest and the lowest values, respectively. Moreover, the partitioning of precipitation into evaporation and runoff from MERRA and NARR does not agree with what was expected, particularly over southern, central, and eastern Quebec. Despite the weaknesses identified, the ability of reanalyses to reproduce the terrestrial water cycle of the recent past (i.e., 1979–2008) remains globally satisfactory. Nonetheless, their potential to provide reliable information must be validated by comparing reanalyses directly with weather stations, especially in remote areas.

Abstract. A far infrared radiometer (FIRR) dedicated to measuring radiation emitted by clear and cloudy atmospheres was developed in the framework of the Thin Ice Clouds in Far InfraRed Experiment (TICFIRE) technology demonstration satellite project. The FIRR detector is an array of 80 × 60 uncooled microbolometers coated with gold black to enhance the absorptivity and responsivity. A filter wheel is used to select atmospheric radiation in nine spectral bands ranging from 8 to 50 µm. Calibrated radiances are obtained using two well-calibrated blackbodies. Images are acquired at a frame rate of 120 Hz, and temporally averaged to reduce electronic noise. A complete measurement sequence takes about 120 s. With a field of view of 6°, the FIRR is not intended to be an imager. Hence spatial average is computed over 193 illuminated pixels to increase the signal-to-noise ratio and consequently the detector resolution. This results in an improvement by a factor of 5 compared to individual pixel measurements. Another threefold increase in resolution is obtained using 193 non-illuminated pixels to remove correlated electronic noise, leading an overall resolution of approximately 0.015 W m−2 sr−1. Laboratory measurements performed on well-known targets suggest an absolute accuracy close to 0.02 W m−2 sr−1, which ensures atmospheric radiance is retrieved with an accuracy better than 1 %. Preliminary in situ experiments performed from the ground in winter and in summer on clear and cloudy atmospheres are compared to radiative transfer simulations. They point out the FIRR ability to detect clouds and changes in relative humidity of a few percent in various atmospheric conditions, paving the way for the development of new algorithms dedicated to ice cloud characterization and water vapor retrieval.

Abstract Increasing evidence indicates that current dynamic global vegetation models (DGVMs) have suffered from insufficient realism and are difficult to improve, particularly because they are built on plant functional type (PFT) schemes. Therefore, new approaches, such as plant trait-based methods, are urgently needed to replace PFT schemes when predicting the distribution of vegetation and investigating vegetation sensitivity. As an important direction towards constructing next-generation DGVMs based on plant functional traits, we propose a novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China. The results demonstrated that a Gaussian mixture model (GMM) trained with a LMA-N mass -LAI data combination yielded an accuracy of 72.82% in simulating vegetation distribution, providing more detailed parameter information regarding community structures and ecosystem functions. The new approach also performed well in analyses of vegetation sensitivity to different climatic scenarios. Although the trait-climate relationship is not the only candidate useful for predicting vegetation distributions and analysing climatic sensitivity, it sheds new light on the development of next-generation trait-based DGVMs.

On the Puzzling Features of Greenland Ice-Core Isotopic Composition; Copenhagen, Denmark, 26–28 October 2015

Abstract This study evaluates the added value in the representation of surface climate variables from an ensemble of regional climate model (RCM) simulations by comparing the relative skill of the RCM simulations and their driving data over a wide range of RCM experimental setups and climate statistics. The methodology is specifically designed to compare results across different variables and metrics, and it incorporates a rigorous approach to separate the added value occurring at different spatial scales. Results show that the RCMs' added value strongly depends on the type of driving data, the climate variable, and the region of interest but depends rather weakly on the choice of the statistical measure, the season, and the RCM physical configuration. Decomposing climate statistics according to different spatial scales shows that improvements are coming from the small scales when considering the representation of spatial patterns, but from the large‐scale contribution in the case of absolute values. Our results also show that a large part of the added value can be attained using some simple postprocessing methods. , Key Points A rigorous methodology that allows evaluating the overall benefits of high‐resolution simulations The most reliable source of added value is the better representation of the spatial variability Substantial added value can also be attained using simple postprocessing methods

Abstract The use of windshields to reduce the impact of wind on snow measurements is common. This paper investigates the catching performance of shielded and unshielded gauges using numerical simulations. In Part II, the role of the windshield and gauge aerodynamics, as well as the varying flow field due to the turbulence generated by the shield–gauge configuration, in reducing the catch efficiency is investigated. This builds on the computational fluid dynamics results obtained in Part I, where the airflow patterns in the proximity of an unshielded and single Alter shielded Geonor T-200B gauge are obtained using both time-independent [Reynolds-averaged Navier–Stokes (RANS)] and time-dependent [large-eddy simulation (LES)] approaches. A Lagrangian trajectory model is used to track different types of snowflakes (wet and dry snow) and to assess the variation of the resulting gauge catching performance with the wind speed. The collection efficiency obtained with the LES approach is generally lower than the one obtained with the RANS approach. This is because of the impact of the LES-resolved turbulence above the gauge orifice rim. The comparison between the collection efficiency values obtained in case of shielded and unshielded gauge validates the choice of installing a single Alter shield in a windy environment. However, time-dependent simulations show that the propagating turbulent structures produced by the aerodynamic response of the upwind single Alter blades have an impact on the collection efficiency. Comparison with field observations provides the validation background for the model results.

Abstract Elevated nitrogen (N) deposition alters the terrestrial carbon (C) cycle, which is likely to feed back to further climate change. However, how the overall terrestrial ecosystem C pools and fluxes respond to N addition remains unclear. By synthesizing data from multiple terrestrial ecosystems, we quantified the response of C pools and fluxes to experimental N addition using a comprehensive meta-analysis method. Our results showed that N addition significantly stimulated soil total C storage by 5.82% ([2.47%, 9.27%], 95% CI, the same below) and increased the C contents of the above- and below-ground parts of plants by 25.65% [11.07%, 42.12%] and 15.93% [6.80%, 25.85%], respectively. Furthermore, N addition significantly increased aboveground net primary production by 52.38% [40.58%, 65.19%] and litterfall by 14.67% [9.24%, 20.38%] at a global scale. However, the C influx from the plant litter to the soil through litter decomposition and the efflux from the soil due to microbial respiration and soil respiration showed insignificant responses to N addition. Overall, our meta-analysis suggested that N addition will increase soil C storage and plant C in both above- and below-ground parts, indicating that terrestrial ecosystems might act to strengthen as a C sink under increasing N deposition.

Abstract The east coast of Australia is regularly influenced by midlatitude cyclones known as East Coast Lows. These form in a range of synoptic situations and are both a cause of severe weather and an important contributor to water security. This paper presents the first projections of future cyclone activity in this region using a regional climate model ensemble, with the use of a range of cyclone identification methods increasing the robustness of results. While there is considerable uncertainty in projections of cyclone frequency during the warm months, there is a robust agreement on a decreased frequency of cyclones during the winter months, when they are most common in the current climate. However, there is a potential increase in the frequency of cyclones with heavy rainfall and those closest to the coast and accordingly those with potential for severe flooding. , Key Points Winter cyclones are projected to decrease on the Australian east coast Cyclones associated with heavy rainfall may increase in frequency Projections of warm season cyclones remain uncertain

Abstract An important source of model uncertainty in climate models arises from unconfined model parameters in physical parameterizations. These parameters are commonly estimated on the basis of manual adjustments (expert tuning), which carries the risk of overtuning the parameters for a specific climate region or time period. This issue is particularly germane in the case of regional climate models (RCMs), which are often developed and used in one or a few geographical regions only. This study addresses the role of objective parameter calibration in this context. Using a previously developed objective calibration methodology, an RCM is calibrated over two regions (Europe and North America) and is used to investigate the transferability of the results. A total of eight different model parameters are calibrated, using a metamodel to account for parameter interactions. The study demonstrates that the calibration is effective in reducing model biases in both domains. For Europe, this concerns in particular a pronounced reduction of the summer warm bias and the associated overestimation of interannual temperature variability that have persisted through previous expert tuning efforts and are common in many global and regional climate models. The key process responsible for this improvement is an increased hydraulic conductivity. Higher hydraulic conductivity increases the water availability at the land surface and leads to increased evaporative cooling, stronger low cloud formation, and associated reduced incoming shortwave radiation. The calibrated parameter values are found to be almost identical for both domains; that is, the parameter calibration is transferable between the two regions. This is a promising result and indicates that models may be more universal than previously considered.

Abstract The aerodynamic response of snow gauges when exposed to the wind is responsible for a significant reduction of their collection performance. The modifications induced by the gauge and the windshield onto the space–time patterns of the undisturbed airflow deviate the snowflake trajectories. In Part I, the disturbed air velocity field in the vicinity of shielded and unshielded gauge configurations is investigated. In Part II, the airflow is the basis for a particle tracking model of snowflake trajectories to estimate the collection efficiency. A Geonor T-200B gauge inside a single Alter shield is simulated for wind speeds varying from 1 to 8 m s−1. Both time-averaged and time-dependent computational fluid dynamics simulations are performed, based on Reynolds-averaged Navier–Stokes (RANS) and large-eddy simulation (LES) models, respectively. A shear stress tensor k–Ω model (where k is the turbulent kinetic energy and Ω is the turbulent specific dissipation rate) is used for the RANS formulation and solved within a finite-volume method. The LES is implemented with a Smagorinsky subgrid-scale method that models the subgrid stresses as a gradient-diffusion process. The RANS simulations confirm the attenuation of the airflow velocity above the gauge when using a single Alter shield, but the generated turbulence above the orifice rim is underestimated. The intensity and spatial extension of the LES-resolved turbulent region show a dependency on the wind speed that was not detected by the RANS. The time-dependent analysis showed the propagation of turbulent structures and the impact on the turbulent kinetic energy above the gauge collecting section.