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Abstract Univariate quantile mapping (QM), a technique often used to statistically postprocess climate simulations, may generate physical inconsistency. This issue is investigated here by classifying physical inconsistency into two types. Type I refers to the attribution of an impossible value to a single variable, and type II refers to the breaking of a fixed intervariable relationship. Here QM is applied to relative humidity (RH) and its parent variables, namely, temperature, pressure, and specific humidity. Twelve sites representing various climate types across North America are investigated. Time series from an ensemble of ten 3-hourly simulations are postprocessed, with the CFSR reanalysis used as the reference product. For type I, results indicate that direct postprocessing of RH generates supersaturation values (>100%) at relatively small frequencies of occurrence. Generated supersaturation amplitudes exceed observed values in fog and clouds. Supersaturation values are generally more frequent and higher when RH is deduced from postprocessed parent variables. For type II, results show that univariate QM practically always breaks the intervariable thermodynamic relationship. Heuristic proxies are designed for comparing the initial bias with physical inconsistency of type II, and results suggest that QM generates a problem that is arguably lesser than the one it is intended to solve. When physical inconsistency is avoided by capping one humidity variable at its saturation level and deducing the other, statistical equivalence with the reference product remains much improved relative to the initial situation. A recommendation for climate services is to postprocess RH and deduce specific humidity rather than the opposite.

ABSTRACT High‐resolution reanalyses offer the potential to improve our understanding of midlatitude cyclones, particularly smaller‐scale systems and those with complex structures. However, previous studies have demonstrated large variations in the frequency and characteristics of Australian midlatitude cyclones between reanalyses when using their native resolution. In this paper we use satellite observations of winds and rainfall in order to evaluate the ability of the ERA‐Interim, JRA55, MERRA and CFSR reanalyses to reproduce Australian east coast cyclones. The MERRA reanalysis produces a large number of erroneous small‐scale lows without cyclonic wind patterns using a simple pressure‐difference‐based cyclone identification and tracking method. Consequently, we recommend the ERA‐Interim reanalysis when using such methods, or applying more complex tracking methods that are able to compensate for these issues.

Abstract Volcanic eruptions can impact the mass balance of ice sheets through changes in climate and the radiative properties of the ice. Yet, empirical evidence highlighting the sensitivity of ancient ice sheets to volcanism is scarce. Here we present an exceptionally well-dated annual glacial varve chronology recording the melting history of the Fennoscandian Ice Sheet at the end of the last deglaciation (∼13,200–12,000 years ago). Our data indicate that abrupt ice melting events coincide with volcanogenic aerosol emissions recorded in Greenland ice cores. We suggest that enhanced ice sheet runoff is primarily associated with albedo effects due to deposition of ash sourced from high-latitude volcanic eruptions. Climate and snowpack mass-balance simulations show evidence for enhanced ice sheet runoff under volcanically forced conditions despite atmospheric cooling. The sensitivity of past ice sheets to volcanic ashfall highlights the need for an accurate coupling between atmosphere and ice sheet components in climate models.

Abstract A devastating, flood-producing rainstorm occurred over southern Alberta, Canada, from 19 to 22 June 2013. The long-lived, heavy rainfall event was a result of complex interplays between topographic, synoptic, and convective processes that rendered an accurate simulation of this event a challenging task. In this study, the Weather Research and Forecasting (WRF) Model was used to simulate this event and was validated against several observation datasets. Both the timing and location of the model precipitation agree closely with the observations, indicating that the WRF Model is capable of reproducing this type of severe event. Sensitivity tests with different microphysics schemes were conducted and evaluated using equitable threat and bias frequency scores. The WRF double-moment 6-class microphysics scheme (WDM6) generally performed better when compared with other schemes. The application of a conventional convective/stratiform separation algorithm shows that convective activity was dominant during the early stages, then evolved into predominantly stratiform precipitation later in the event. The HYSPLIT back-trajectory analysis and regional water budget assessments using WRF simulation output suggest that the moisture for the precipitation was mainly from recycling antecedent soil moisture through evaporation and evapotranspiration over the Canadian Prairies and the U.S. Great Plains. This analysis also shows that a small fraction of the moisture can be traced back to the northeastern Pacific, and direct uptake from the Gulf of Mexico was not a significant source in this event.

Abstract Several cloud retrieval algorithms based on satellite observations in the infrared have been developed in the last decades. However, these observations only cover the midinfrared (MIR, λ < 15 μm) part of the spectrum, and none are available in the far‐infrared (FIR, λ ≥ 15 μm). Using the optimal estimation method, we show that adding a few FIR channels to existing spaceborne radiometers would significantly improve their ability to retrieve ice cloud radiative properties. For clouds encountered in the polar regions and the upper troposphere, where the atmosphere is sufficiently transparent in the FIR, using FIR channels would reduce by more than 50% the uncertainties on retrieved values of optical thickness, effective particle diameter, and cloud top altitude. Notably, this would extend the range of applicability of current retrieval methods to the polar regions and to clouds with large optical thickness, where MIR algorithms perform poorly. The high performance of solar reflection‐based algorithms would thus be reached in nighttime conditions. Since the sensitivity of ice cloud thermal emission to effective particle diameter is approximately 5 times larger in the FIR than in the MIR, using FIR observations is a promising venue for studying ice cloud microphysics and precipitation processes. This is highly relevant for cirrus clouds and convective towers. This is also essential to study precipitation in the driest regions of the atmosphere, where strong feedbacks are at play between clouds and water vapor. The deployment in the near future of a FIR spaceborne radiometer is technologically feasible and should be strongly supported. , Plain Language Summary The size of ice cloud particles can be estimated from space by measuring the infrared emission of ice clouds. However, this method no longer works when clouds are too thick or when particles are too large, although such clouds are encountered in many regions of the Earth and are critical for the Earth's climate. Using new sensors that measure cloud emission at longer wavelengths, in the so‐called far‐infrared region, would extend the potential of satellite observations to thick clouds, to clouds made of larger particles, and to clouds found in the polar regions which are very poorly known. These new sensors have become available in the last years, but none is deployed in space so far. We show that a satellite equipped with a few channels in the far‐infrared would greatly increase our capacity to observe ice clouds. This is promising for our understanding of cloud microphysics and precipitation in general, and for studying the water cycle in the polar regions in particular. For these reasons, it is highly recommended to deploy a far‐infrared radiometer in space. , Key Points Ice cloud remote sensing would be greatly improved by adding far‐infrared channels to existing midinfrared spaceborne radiometers Far‐infrared radiometry is well suited to study ice clouds in the polar regions and upper troposphere Using far‐infrared radiometry extends current infrared capabilities to large optical thickness and large cloud particles

Abstract We analyze, using Poisson regressions, the main climate influences on North Atlantic tropical cyclone activity. The analysis is performed using not only various time series of basin‐wide storm counts but also various series of regional clusters, taking into account shortcomings of the hurricane database through estimates of missing storms. The analysis confirms that tropical cyclones forming in different regions of the Atlantic are susceptible to different climate influences. We also investigate the presence of trends in these various time series, both at the basin‐wide and cluster levels, and show that, even after accounting for possible missing storms, there remains an upward trend in the eastern part of the basin and a downward trend in the western part. Using model selection algorithms, we show that the best model of Atlantic tropical cyclone activity for the recent past is constructed using Atlantic sea surface temperature and upper tropospheric temperature, while for the 1878–2015 period, the chosen covariates are Atlantic sea surface temperature and El Niño–Southern Oscillation. We also note that the presence of these artificial trends can impact the selection of the best covariates. If the underlying series shows an upward trend, then the mean Atlantic sea surface temperature captures both interannual variability and the upward trend, artificial or not. The relative sea surface temperature is chosen instead for stationary counts. Finally, we show that the predictive capability of the statistical models investigated is low for U.S. landfalling hurricanes but can be considerably improved when forecasting combinations of clusters whose hurricanes are most likely to make landfall. , Key Points Estimates of missing storms are not sufficient to account for the increase in hurricane activity in the eastern tropical Atlantic Recent upward trends, artificial or not, affect the selection of key determinants of tropical cyclone activity, especially the SST variable Despite previous results to that effect, the May–June NAO does not provide predictive skill for Atlantic landfalling hurricanes

Abstract Data obtained from a variety of sources including the Canadian Lightning Detection Network, weather radars, weather stations and operational numerical weather model analyses were used to address the evolution of precipitation during the June 2013 southern Alberta flood. The event was linked to a mid‐level closed low pressure system to the west of the region and a surface low pressure region initially to its south. This configuration brought warm, moist unstable air into the region that led to dramatic, organized convection with an abundance of lightning and some hail. Such conditions occurred in the southern parts of the region whereas the northern parts were devoid of lightning. Initially, precipitation rates were high (extreme 15‐min rainfall rates up to 102 mm h −1 were measured) but decreased to lower values as the precipitation shifted to long‐lived stratiform conditions. Both the convective and stratiform precipitation components were affected by the topography. Similar flooding events, such as June 2002, have occurred over this region although the 2002 event was colder and precipitation was not associated with substantial convection over southwest Alberta. Copyright © 2016 Her Majesty the Queen in Right of Canada. Hydrological Processes. © John Wiley & Sons, Ltd.

Abstract In June 2013, excessive rainfall associated with an intense weather system triggered severe flooding in southern Alberta, which became the costliest natural disaster in Canadian history. This article provides an overview of the climatological aspects and large‐scale hydrometeorological features associated with the flooding event based upon information from a variety of sources, including satellite data, upper air soundings, surface observations and operational model analyses. The results show that multiple factors combined to create this unusually severe event. The event was characterized by a slow‐moving upper level low pressure system west of Alberta, blocked by an upper level ridge, while an associated well‐organized surface low pressure system kept southern Alberta, especially the eastern slopes of the Rocky Mountains, in continuous precipitation for up to two days. Results from air parcel trajectory analysis show that a significant amount of the moisture originated from the central Great Plains, transported into Alberta by a southeasterly low level jet. The event was first dominated by significant thunderstorm activity, and then evolved into continuous precipitation supported by the synoptic‐scale low pressure system. Both the thunderstorm activity and upslope winds associated with the low pressure system produced large rainfall amounts. A comparison with previous similar events occurring in the same region suggests that the synoptic‐scale features associated with the 2013 rainfall event were not particularly intense; however, its storm environment was the most convectively unstable. The system also exhibited a relatively high freezing level, which resulted in rain, rather than snow, mainly falling over the still snow‐covered mountainous areas. Melting associated with this rain‐on‐snow scenario likely contributed to downstream flooding. Furthermore, above‐normal snowfall in the preceding spring helped to maintain snow in the high‐elevation areas, which facilitated the rain‐on‐snow event. Copyright © 2016 John Wiley & Sons, Ltd.

The science of complex systems is increasingly asked to forecast the consequences of climate change. As a result, scientists are now engaged in making predictions about an uncertain future, which entails the efficient communication of this uncertainty. Here we show the benefits of hierarchically decomposing the uncertainty in predicted changes in animal population size into its components due to structural uncertainty in climate scenarios (greenhouse gas emissions and global circulation models), structural uncertainty in the demographic model, climatic stochasticity, environmental stochasticity unexplained by climate–demographic trait relationships, and sampling variance in demographic parameter estimates. We quantify components of uncertainty surrounding the future abundance of a migratory bird, the greater snow goose ( Chen caeruslescens atlantica ), using a process-based demographic model covering their full annual cycle. Our model predicts a slow population increase but with a large prediction uncertainty. As expected from theoretical variance decomposition rules, the contribution of sampling variance to prediction uncertainty rapidly overcomes that of process variance and dominates. Among the sources of process variance, uncertainty in the climate scenarios contributed less than 3% of the total prediction variance over a 40-year period, much less than environmental stochasticity. Our study exemplifies opportunities to improve the forecasting of complex systems using long-term studies and the challenges inherent to predicting the future of stochastic systems.