Can climate models represent the precipitation associated with extratropical cyclones?

Extratropical cyclones produce the majority of precipitation in many regions of the extratropics. This study evaluates the ability of a climate model, HiGEM, to reproduce the precipitation associated with extratropical cyclones. The model is evaluated using the ERA-Interim reanalysis and GPCP dataset. The analysis employs a cyclone centred compositing technique, evaluates composites across a range of geographical areas and cyclone intensities and also investigates the ability of the model to reproduce the climatological distribution of cyclone associated precipitation across the Northern Hemisphere. Using this phenomena centred approach provides an ability to identify the processes which are responsible for climatological biases in the model. Composite precipitation intensities are found to be comparable when all cyclones across the Northern Hemisphere are included. When the cyclones are filtered by region or intensity, differences are found, in particular, HiGEM produces too much precipitation in its most intense cyclones relative to ERA-Interim and GPCP. Biases in the climatological distribution of cyclone associated precipitation are also found, with biases around the storm track regions associated with both the number of cyclones in HiGEM and also their average precipitation intensity. These results have implications for the reliability of future projections of extratropical precipitation from the model.

Seasonal forecasts of tropical cyclone activity in a high-atmospheric-resolution coupled prediction system

Seasonal forecast skill of the basinwide and regional tropical cyclone (TC) activity in an experimental coupled prediction system based on the ECMWF System 4 is assessed. As part of a collaboration between the Center for Ocean–Land–Atmosphere Studies (COLA) and the ECMWF called Project Minerva, the system is integrated at the atmospheric horizontal spectral resolutions of T319, T639, and T1279. Seven-month hindcasts starting from 1 May for the years 1980–2011 are produced at all three resolutions with at least 15 ensemble members. The Minerva system demonstrates statistically significant skill for retrospective forecasts of TC frequency and accumulated cyclone energy (ACE) in the North Atlantic (NA), eastern North Pacific (EP), and western North Pacific. While the highest scores overall are achieved in the North Pacific, the skill in the NA appears to be limited by an overly strong influence of the tropical Pacific variability. Higher model resolution improves skill scores for the ACE and, to a lesser extent, the TC frequency, even though the influence of large-scale climate variations on these TC activity measures is largely independent of resolution changes. The biggest gain occurs in transition from T319 to T639. Significant skill in regional TC forecasts is achieved over broad areas of the Northern Hemisphere. The highest-resolution hindcasts exhibit additional locations with skill in the NA and EP, including land-adjacent areas. The feasibility of regional intensity forecasts is assessed. In the presence of the coupled model biases, the benefits of high resolution for seasonal TC forecasting may be underestimated.

The Climate-system Historical Forecast Project: do stratosphere-resolving models make better seasonal climate predictions in boreal winter?

Using an international, multi-model suite of historical forecasts from the World Climate Research Programme (WCRP) Climate-system Historical Forecast Project (CHFP), we compare the seasonal prediction skill in boreal wintertime between models that resolve the stratosphere and its dynamics (“high-top”) and models that do not (“low-top”). We evaluate hindcasts that are initialized in November, and examine the model biases in the stratosphere and how they relate to boreal wintertime (Dec-Mar) seasonal forecast skill. We are unable to detect more skill in the high-top ensemble-mean than the low-top ensemble-mean in forecasting the wintertime North Atlantic Oscillation, but model performance varies widely. Increasing the ensemble size clearly increases the skill for a given model. We then examine two major processes involving stratosphere-troposphere interactions (the El Niño-Southern Oscillation/ENSO and the Quasi-biennial Oscillation/QBO) and how they relate to predictive skill on intra-seasonal to seasonal timescales, particularly over the North Atlantic and Eurasia regions. High-top models tend to have a more realistic stratospheric response to El Niño and the QBO compared to low-top models. Enhanced conditional wintertime skill over high-latitudes and the North Atlantic region during winters with El Niño conditions suggests a possible role for a stratospheric pathway.

A regionally informed abundance index for supporting integrative analyses across butterfly monitoring schemes

1. The rapid expansion of systematic monitoring schemes necessitates robust methods to reliably assess species' status and trends. Insect monitoring poses a challenge where there are strong seasonal patterns, requiring repeated counts to reliably assess abundance. Butterfly monitoring schemes (BMSs) operate in an increasing number of countries with broadly the same methodology, yet they differ in their observation frequency and in the methods used to compute annual abundance indices. 2. Using simulated and observed data, we performed an extensive comparison of two approaches used to derive abundance indices from count data collected via BMS, under a range of sampling frequencies. Linear interpolation is most commonly used to estimate abundance indices from seasonal count series. A second method, hereafter the regional generalized additive model (GAM), fits a GAM to repeated counts within sites across a climatic region. For the two methods, we estimated bias in abundance indices and the statistical power for detecting trends, given different proportions of missing counts. We also compared the accuracy of trend estimates using systematically degraded observed counts of the Gatekeeper Pyronia tithonus (Linnaeus 1767). 3. The regional GAM method generally outperforms the linear interpolation method. When the proportion of missing counts increased beyond 50%, indices derived via the linear interpolation method showed substantially higher estimation error as well as clear biases, in comparison to the regional GAM method. The regional GAM method also showed higher power to detect trends when the proportion of missing counts was substantial. 4. Synthesis and applications. Monitoring offers invaluable data to support conservation policy and management, but requires robust analysis approaches and guidance for new and expanding schemes. Based on our findings, we recommend the regional generalized additive model approach when conducting integrative analyses across schemes, or when analysing scheme data with reduced sampling efforts. This method enables existing schemes to be expanded or new schemes to be developed with reduced within-year sampling frequency, as well as affording options to adapt protocols to more efficiently assess species status and trends across large geographical scales.

Understanding climate change projections for precipitation over western Europe with a weather typing approach

Precipitation over western Europe (WE) is projected to increase (decrease) roughly northward (equatorward) of 50°N during the 21st century. These changes are generally attributed to alterations in the regional large-scale circulation, e.g., jet stream, cyclone activity, and blocking frequencies. A novel weather typing within the sector (30°W–10°E, 25–70°N) is used for a more comprehensive dynamical interpretation of precipitation changes. A k-means clustering on daily mean sea level pressure was undertaken for ERA-Interim reanalysis (1979–2014). Eight weather types are identified: S1, S2, S3 (summertime types), W1, W2, W3 (wintertime types), B1, and B2 (blocking-like types). Their distinctive dynamical characteristics allow identifying the main large-scale precipitation-driving mechanisms. Simulations with 22 Coupled Model Intercomparison Project 5 models for recent climate conditions show biases in reproducing the observed seasonality of weather types. In particular, an overestimation of weather type frequencies associated with zonal airflow is identified. Considering projections following the (Representative Concentration Pathways) RCP8.5 scenario over 2071–2100, the frequencies of the three driest types (S1, B2, and W3) are projected to increase (mainly S1, +4%) in detriment of the rainiest types, particularly W1 (−3%). These changes explain most of the precipitation projections over WE. However, a weather type-independent background signal is identified (increase/decrease in precipitation over northern/southern WE), suggesting modifications in precipitation-generating processes and/or model inability to accurately simulate these processes. Despite these caveats in the precipitation scenarios for WE, which must be duly taken into account, our approach permits a better understanding of the projected trends for precipitation over WE.

Are visual threats prioritized without awareness? A critical review and meta analysis involving 3 behavioral paradigms and 2696 observers

Given capacity limits, only a subset of stimuli 1 give rise to a conscious percept. Neurocognitive models suggest that humans have evolved mechanisms that operate without awareness and prioritize threatening stimuli over neutral stimuli in subsequent perception. In this meta analysis, we review evidence for this ‘standard hypothesis’ emanating from three widely used, but rather different experimental paradigms that have been used to manipulate awareness. We found a small pooled threat-bias effect in the masked visual probe paradigm, a medium effect in the binocular rivalry paradigm and highly inconsistent effects in the breaking continuous flash suppression paradigm. Substantial heterogeneity was explained by the stimulus type: the only threat stimuli that were robustly prioritized across all three paradigms were fearful faces. Meta regression revealed that anxiety may modulate threat biases, but only under specific presentation conditions. We also found that insufficiently rigorous awareness measures, inadequate control of response biases and low level confounds may undermine claims of genuine unconscious threat processing. Considering the data together, we suggest that uncritical acceptance of the standard hypothesis is premature: current behavioral evidence for threat-sensitive visual processing that operates without awareness is weak.

Determinants of green building adoption

In this paper we investigate variations in the adoption of LEED-certified commercial buildings across 174 core-based statistical areas in the United States. Drawing upon a unique database and using a robust analytical framework, the determinants of the proportion LEED-certified space are modeled. We find that, despite high growth rates, LEED-certified stock accounts for a relatively small proportion of the total commercial stock. The average proportion is less than 1%. A further contribution of the paper is that our concentration measure avoids the biases associated with simple percentage measures that were used in previous studies of this topic. Strongest predictors of the proportion of LEED-certified commercial space in a local market are market size, educational attainment and economic growth. In terms of policy effectiveness, it is found that only a mandatory requirement to obtain LEED certification for new buildings has a significant positive effect on market penetration.

The SPARC Data Initiative: comparisons of CFC-11, CFC-12, HF and SF6 climatologies from international satellite limb sounders

A quality assessment of the CFC-11 (CCl3F), CFC-12 (CCl2F2), HF, and SF6 products from limb-viewing satellite instruments is provided by means of a detailed intercomparison. The climatologies in the form of monthly zonal mean time series are obtained from HALOE, MIPAS, ACE-FTS, and HIRDLS within the time period 1991–2010. The intercomparisons focus on the mean biases of the monthly and annual zonal mean fields and aim to identify their vertical, latitudinal and temporal structure. The CFC evaluations (based on MIPAS, ACE-FTS and HIRDLS) reveal that the uncertainty in our knowledge of the atmospheric CFC-11 and CFC-12 mean state, as given by satellite data sets, is smallest in the tropics and mid-latitudes at altitudes below 50 and 20 hPa, respectively, with a 1σ multi-instrument spread of up to ±5 %. For HF, the situation is reversed. The two available data sets (HALOE and ACE-FTS) agree well above 100 hPa, with a spread in this region of ±5 to ±10 %, while at altitudes below 100 hPa the HF annual mean state is less well known, with a spread ±30 % and larger. The atmospheric SF6 annual mean states derived from two satellite data sets (MIPAS and ACE-FTS) show only very small differences with a spread of less than ±5 % and often below ±2.5 %. While the overall agreement among the climatological data sets is very good for large parts of the upper troposphere and lower stratosphere (CFCs, SF6) or middle stratosphere (HF), individual discrepancies have been identified. Pronounced deviations between the instrument climatologies exist for particular atmospheric regions which differ from gas to gas. Notable features are differently shaped isopleths in the subtropics, deviations in the vertical gradients in the lower stratosphere and in the meridional gradients in the upper troposphere, and inconsistencies in the seasonal cycle. Additionally, long-term drifts between the instruments have been identified for the CFC-11 and CFC-12 time series. The evaluations as a whole provide guidance on what data sets are the most reliable for applications such as studies of atmospheric transport and variability, model–measurement comparisons and detection of long-term trends. The data sets will be publicly available from the SPARC Data Centre and through PANGAEA (doi:10.1594/PANGAEA.849223).

Constraining the aerosol influence on cloud fraction

Aerosol-cloud interactions have the potential to modify many different cloud properties. There is significant uncertainty in the strength of these aerosol-cloud interactions in analyses of observational data, partly due to the difficulty in separating aerosol effects on clouds from correlations generated by local meteorology. The relationship between aerosol and cloud fraction (CF) is particularly important to determine, due to the strong correlation of CF to other cloud properties and its large impact on radiation. It has also been one of the hardest to quantify from satellites due to the strong meteorological covariations involved. This work presents a new method to analyze the relationship between aerosol optical depth (AOD) and CF. By including information about the cloud droplet number concentration (CDNC), the impact of the meteorological covariations is significantly reduced. This method shows that much of the AOD-CF correlation is explained by relationships other than that mediated by CDNC. By accounting for these, the strength of the global mean AOD-CF relationship is reduced by around 80%. This suggests that the majority of the AOD-CF relationship is due to meteorological covariations, especially in the shallow cumulus regime. Requiring CDNC to mediate the AOD-CF relationship implies an effective anthropogenic radiative forcing from an aerosol influence on liquid CF of −0.48 W m−2 (−0.1 to −0.64 W m−2), although some uncertainty remains due to possible biases in the CDNC retrievals in broken cloud scenes.

High-precision measurements of the co-polar correlation coefficient: non-Gaussian errors and retrieval of the dispersion parameter µ in rainfall

The co-polar correlation coefficient (ρhv) has many applications, including hydrometeor classification, ground clutter and melting layer identification, interpretation of ice microphysics and the retrieval of rain drop size distributions (DSDs). However, we currently lack the quantitative error estimates that are necessary if these applications are to be fully exploited. Previous error estimates of ρhv rely on knowledge of the unknown "true" ρhv and implicitly assume a Gaussian probability distribution function of ρhv samples. We show that frequency distributions of ρhv estimates are in fact highly negatively skewed. A new variable: L = -log10(1 - ρhv) is defined, which does have Gaussian error statistics, and a standard deviation depending only on the number of independent radar pulses. This is verified using observations of spherical drizzle drops, allowing, for the first time, the construction of rigorous confidence intervals in estimates of ρhv. In addition, we demonstrate how the imperfect co-location of the horizontal and vertical polarisation sample volumes may be accounted for. The possibility of using L to estimate the dispersion parameter (µ) in the gamma drop size distribution is investigated. We find that including drop oscillations is essential for this application, otherwise there could be biases in retrieved µ of up to ~8. Preliminary results in rainfall are presented. In a convective rain case study, our estimates show µ to be substantially larger than 0 (an exponential DSD). In this particular rain event, rain rate would be overestimated by up to 50% if a simple exponential DSD is assumed.

Bayesian analysis of uncertainty in the GlobCover 2009 land cover product at climate model grid scale

Land cover data derived from satellites are commonly used to prescribe inputs to models of the land surface. Since such data inevitably contains errors, quantifying how uncertainties in the data affect a model’s output is important. To do so, a spatial distribution of possible land cover values is required to propagate through the model’s simulation. However, at large scales, such as those required for climate models, such spatial modelling can be difficult. Also, computer models often require land cover proportions at sites larger than the original map scale as inputs, and it is the uncertainty in these proportions that this article discusses. This paper describes a Monte Carlo sampling scheme that generates realisations of land cover proportions from the posterior distribution as implied by a Bayesian analysis that combines spatial information in the land cover map and its associated confusion matrix. The technique is computationally simple and has been applied previously to the Land Cover Map 2000 for the region of England and Wales. This article demonstrates the ability of the technique to scale up to large (global) satellite derived land cover maps and reports its application to the GlobCover 2009 data product. The results show that, in general, the GlobCover data possesses only small biases, with the largest belonging to non–vegetated surfaces. In vegetated surfaces, the most prominent area of uncertainty is Southern Africa, which represents a complex heterogeneous landscape. It is also clear from this study that greater resources need to be devoted to the construction of comprehensive confusion matrices.

Simulating decadal variability in the North Atlantic Ocean

Observations and climate models suggest significant decadal variability within the North Atlantic subpolar gyre (NA SPG), though observations are sparse and models disagree on the details of this variability. Therefore, it is important to understand 1) the mechanisms of simulated decadal variability, 2) which parts of simulated variability are more faithful representations of reality, and 3) the implications for climate predictions. Here, we investigate the decadal variability in the NA SPG in the state-of-the-art, high resolution (0.25◦ ocean resolution), climate model ‘HadGEM3’. We find a decadal mode with a period of 17 years that explains 30% of the annual variance in related indices. The mode arises due to the advection of heat content anomalies, and shows asymmetries in the timescale of phase reversal between positive and negative phases. A negative feedback from temperature-driven density anomalies in the Labrador Sea (LS) allows for the phase reversal. The North Atlantic Oscillation (NAO), which exhibits the same periodicity, amplifies the mode. The atmosphere-ocean coupling is stronger during positive rather than negative NAO states, explaining the asymmetry. Within the NA SPG, there is potential predictability arising partly from this mode for up to 5 years. There are important similarities between observed and simulated variability, such as the apparent role for the propagation of heat content anomalies. However, observations suggest interannual LS density anomalies are salinity-driven. Salinity control of density would change the temperature feedback to the south, possibly limiting real-world predictive skill in the southern NA SPG with this model. Finally, to understand the diversity of behaviours, we analyse 42 present-generation climate models. Temperature and salinity biases are found to systematically influence the driver of density variability in the LS. Resolution is a good predictor of the biases. The dependence of variability on the background state has important implications for decadal predictions.

Arctic Snow Microstructure Experiment for the development of snow emission modelling

The Arctic Snow Microstructure Experiment (ASMEx) took place in Sodankylä, Finland in the winters of 2013-2014 and 2014-2015. Radiometric, macro-, and microstructure measurements were made under different experimental conditions of homogenous snow slabs, extracted from the natural seasonal taiga snowpack. Traditional and modern measurement techniques were used for snow macro- and microstructure observations. Radiometric measurements of the microwave emission of snow on reflector and absorber bases were made at frequencies 18.7, 21.0, 36.5, 89.0 and 150.0 GHz, for both horizontal and vertical polarizations. Two measurement configurations were used for radiometric measurements: a reflecting surface and an absorbing base beneath the snow slabs. Simulations of brightness temperatures using two microwave emission models, Helsinki University of Technology (HUT) snow emission model and Microwave Emission Model of Layered Snowpacks (MEMLS), were compared to observed brightness temperatures. RMSE and bias were calculated; with the RMSE and bias values being smallest upon an absorbing base at vertical polarization. Simulations overestimated the brightness temperatures on absorbing base cases at horizontal polarization. With the other experimental conditions, the biases were small; with the exception of the HUT model 36.5 GHz simulation, which produced an underestimation for the reflector base cases. This experiment provides a solid framework for future research on the extinction of microwave radiation inside snow.

Large contribution of supercooled liquid clouds to the solar radiation budget of the Southern Ocean

The Southern Ocean is a critical region for global climate, yet large cloud and solar radiation biases over the Southern Ocean are a long-standing problem in climate models and are poorly understood, leading to biases in simulated sea surface temperatures. This study shows that supercooled liquid clouds are central to understanding and simulating the Southern Ocean environment. A combination of satellite observational data and detailed radiative transfer calculations is used to quantify the impact of cloud phase and cloud vertical structure on the reflected solar radiation in the Southern Hemisphere summer. It is found that clouds with supercooled liquid tops dominate the population of liquid clouds. The observations show that clouds with supercooled liquid tops contribute between 27% and 38% to the total reflected solar radiation between 40° and 70°S, and climate models are found to poorly simulate these clouds. The results quantify the importance of supercooled liquid clouds in the Southern Ocean environment and highlight the need to improve understanding of the physical processes that control these clouds in order to improve their simulation in numerical models. This is not only important for improving the simulation of present-day climate and climate variability, but also relevant for increasing confidence in climate feedback processes and future climate projections.

Cosmic shear requirements on the wavelength dependence of telescope point spread functions

Cosmic shear requires high precision measurement of galaxy shapes in the presence of the observational point spread function (PSF) that smears out the image. The PSF must therefore be known for each galaxy to a high accuracy. However, for several reasons, the PSF is usually wavelength dependent; therefore, the differences between the spectral energy distribution of the observed objects introduce further complexity. In this paper, we investigate the effect of the wavelength dependence of the PSF, focusing on instruments in which the PSF size is dominated by the diffraction limit of the telescope and which use broad-band filters for shape measurement. We first calculate biases on cosmological parameter estimation from cosmic shear when the stellar PSF is used uncorrected. Using realistic galaxy and star spectral energy distributions and populations and a simple three-component circular PSF, we find that the colour dependence must be taken into account for the next generation of telescopes. We then consider two different methods for removing the effect: (i) the use of stars of the same colour as the galaxies and (ii) estimation of the galaxy spectral energy distribution using multiple colours and using a telescope model for the PSF. We find that both of these methods correct the effect to levels below the tolerances required for per cent level measurements of dark energy parameters. Comparison of the two methods favours the template-fitting method because its efficiency is less dependent on galaxy redshift than the broad-band colour method and takes full advantage of deeper photometry.