Basic concepts for convection parameterization in weather forecast and climate models: COST Action ES0905 final report

The research network “Basic Concepts for Convection Parameterization in Weather Forecast and Climate Models” was organized with European funding (COST Action ES0905) for the period of 2010–2014. Its extensive brainstorming suggests how the subgrid-scale parameterization problem in atmospheric modeling, especially for convection, can be examined and developed from the point of view of a robust theoretical basis. Our main cautions are current emphasis on massive observational data analyses and process studies. The closure and the entrainment–detrainment problems are identified as the two highest priorities for convection parameterization under the mass–flux formulation. The need for a drastic change of the current European research culture as concerns policies and funding in order not to further deplete the visions of the European researchers focusing on those basic issues is emphasized.

HESS Opinions: On forecast (in)consistency in a hydro-meteorological chain: curse or blessing?

Flood forecasting increasingly relies on numerical weather prediction forecasts to achieve longer lead times. One of the key difficulties that is emerging in constructing a decision framework for these flood forecasts is what to dowhen consecutive forecasts are so different that they lead to different conclusions regarding the issuing of warnings or triggering other action. In this opinion paper we explore some of the issues surrounding such forecast inconsistency (also known as "Jumpiness", "Turning points", "Continuity" or number of "Swings"). In thsi opinion paper we define forecast inconsistency; discuss the reasons why forecasts might be inconsistent; how we should analyse inconsistency; and what we should do about it; how we should communicate it and whether it is a totally undesirable property. The property of consistency is increasingly emerging as a hot topic in many forecasting environments.

A new understanding of El Niño's impact over East Asia: dominance of the ENSO combination mode

Previous studies have shown that the Indo-Pacific atmospheric response to ENSO comprises two dominant modes of variability: a meridionally quasi-symmetric response (independent from the annual cycle) and an anti-symmetric response (arising from the nonlinear atmospheric interaction between ENSO variability and the annual cycle), referred to as the combination mode (C-Mode). This study demonstrates that the direct El Niño signal over the tropics is confined to the equatorial region and has no significant impact on the atmospheric response over East Asia. The El Niño-associated equatorial anomalies can be expanded towards off-equatorial regions by the C-Mode through ENSO’s interaction with the annual cycle. The C-Mode is the prime driver for the development of an anomalous low-level anticyclone over the western North Pacific (WNP) during the El Niño decay phase, which usually transports more moisture to East Asia and thereby causes more precipitation over southern China. We use an Atmospheric General Circulation Model that well reproduces the WNP anticyclonic anomalies when both El Niño sea surface temperature (SST) anomalies as well as the SST annual cycle are prescribed as boundary conditions. However, no significant WNP anticyclonic circulation anomaly appears during the El Niño decay phase when excluding the SST annual cycle. Our analyses of observational data and model experiments suggest that the annual cycle plays a key role in the East Asian climate anomalies associated with El Niño through their nonlinear atmospheric interaction. Hence, a realistic simulation of the annual cycle is crucial in order to correctly capture the ENSO-associated climate anomalies over East Asia.

Long term individual load forecast under different electrical vehicles uptake scenarios

More and more households are purchasing electric vehicles (EVs), and this will continue as we move towards a low carbon future. There are various projections as to the rate of EV uptake, but all predict an increase over the next ten years. Charging these EVs will produce one of the biggest loads on the low voltage network. To manage the network, we must not only take into account the number of EVs taken up, but where on the network they are charging, and at what time. To simulate the impact on the network from high, medium and low EV uptake (as outlined by the UK government), we present an agent-based model. We initialise the model to assign an EV to a household based on either random distribution or social influences - that is, a neighbour of an EV owner is more likely to also purchase an EV. Additionally, we examine the effect of peak behaviour on the network when charging is at day-time, night-time, or a mix of both. The model is implemented on a neighbourhood in south-east England using smart meter data (half hourly electricity readings) and real life charging patterns from an EV trial. Our results indicate that social influence can increase the peak demand on a local level (street or feeder), meaning that medium EV uptake can create higher peak demand than currently expected.

Rainfall estimation by rain gauge-radar combination: a concurrent multiplicative-additive approach

A procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) is proposed for operational rainfall estimation using rain gauges and radar data. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on Barnes' objective analysis scheme (OAS), whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km2. Results generally indicated an improved quality with respect to other methods evaluated: a standard mean-field bias adjustment, a spatially variable adjustment with multiplicative factors, and ordinary cokriging.

Finite sample weighting of recursive forecast errors

This paper proposes and tests a new framework for weighting recursive out-of-sample prediction errors according to their corresponding levels of in-sample estimation uncertainty. In essence, we show how to use the maximum possible amount of information from the sample in the evaluation of the prediction accuracy, by commencing the forecasts at the earliest opportunity and weighting the prediction errors. Via a Monte Carlo study, we demonstrate that the proposed framework selects the correct model from a set of candidate models considerably more often than the existing standard approach when only a small sample is available. We also show that the proposed weighting approaches result in tests of equal predictive accuracy that have much better sizes than the standard approach. An application to an exchange rate dataset highlights relevant differences in the results of tests of predictive accuracy based on the standard approach versus the framework proposed in this paper.

Xylo-oligosaccharides alone or in synbiotic combination with Bifidobacterium animalis subsp. lactis induce bifidogenesis and modulate markers of immune function in healthy adults: a double-blind, placebo-controlled, randomised, factorial cross-over study.

Prebiotics, probiotics and synbiotics are dietary ingredients with the potential to influence health and mucosal and systemic immune function by altering the composition of the gut microbiota. In the present study, a candidate prebiotic (xylo-oligosaccharide, XOS, 8 g/d), probiotic (Bifidobacterium animalis subsp. lactis Bi-07, 109 colony-forming units (CFU)/d) or synbiotic (8 g XOS+109 CFU Bi-07/d) was given to healthy adults (25–65 years) for 21 d. The aim was to identify the effect of the supplements on bowel habits, self-reported mood, composition of the gut microbiota, blood lipid concentrations and immune function. XOS supplementation increased mean bowel movements per d (P= 0·009), but did not alter the symptoms of bloating, abdominal pain or flatulence or the incidence of any reported adverse events compared with maltodextrin supplementation. XOS supplementation significantly increased participant-reported vitality (P= 0·003) and happiness (P= 0·034). Lowest reported use of analgesics was observed during the XOS+Bi-07 supplementation period (P= 0·004). XOS supplementation significantly increased faecal bifidobacterial counts (P= 0·008) and fasting plasma HDL concentrations (P= 0·005). Bi-07 supplementation significantly increased faecal B. lactis content (P= 0·007), lowered lipopolysaccharide-stimulated IL-4 secretion in whole-blood cultures (P= 0·035) and salivary IgA content (P= 0·040) and increased IL-6 secretion (P= 0·009). XOS supplementation resulted in lower expression of CD16/56 on natural killer T cells (P= 0·027) and lower IL-10 secretion (P= 0·049), while XOS and Bi-07 supplementation reduced the expression of CD19 on B cells (XOS × Bi-07, P= 0·009). The present study demonstrates that XOS induce bifidogenesis, improve aspects of the plasma lipid profile and modulate the markers of immune function in healthy adults. The provision of XOS+Bi-07 as a synbiotic may confer further benefits due to the discrete effects of Bi-07 on the gut microbiota and markers of immune function.

Klebsiella pneumoniae subsp. pneumoniae–bacteriophage combination from the caecal effluent of a healthy woman

A sample of caecal effluent was obtained from a female patient who had undergone a routine colonoscopic examination. Bacteria were isolated anaerobically from the sample, and screened against the remaining filtered caecal effluent in an attempt to isolate bacteriophages (phages). A lytic phage, named KLPN1, was isolated on a strain identified as Klebsiella pneumoniae subsp. pneumoniae (capsular type K2, rmpA+). This Siphoviridae phage presents a rosette-like tail tip and exhibits depolymerase activity, as demonstrated by the formation of plaque-surrounding haloes that increased in size over the course of incubation. When screened against a panel of clinical isolates of K. pneumoniae subsp. pneumoniae, phage KLPN1 was shown to infect and lyse capsular type K2 strains, though it did not exhibit depolymerase activity on such hosts. The genome of KLPN1 was determined to be 49,037 bp (50.53 %GC) in length, encompassing 73 predicted ORFs, of which 23 represented genes associated with structure, host recognition, packaging, DNA replication and cell lysis. On the basis of sequence analyses, phages KLPN1 (GenBank: KR262148) and 1513 (a member of the family Siphoviridae, GenBank: KP658157) were found to be two new members of the genus “Kp36likevirus”.

How is the balance of a forecast ensemble affected by adaptive and non-adaptive localization schemes?

This paper investigates the effect on balance of a number of Schur product-type localization schemes which have been designed with the primary function of reducing spurious far-field correlations in forecast error statistics. The localization schemes studied comprise a non-adaptive scheme (where the moderation matrix is decomposed in a spectral basis), and two adaptive schemes, namely a simplified version of SENCORP (Smoothed ENsemble COrrelations Raised to a Power) and ECO-RAP (Ensemble COrrelations Raised to A Power). The paper shows, we believe for the first time, how the degree of balance (geostrophic and hydrostatic) implied by the error covariance matrices localized by these schemes can be diagnosed. Here it is considered that an effective localization scheme is one that reduces spurious correlations adequately but also minimizes disruption of balance (where the 'correct' degree of balance or imbalance is assumed to be possessed by the unlocalized ensemble). By varying free parameters that describe each scheme (e.g. the degree of truncation in the schemes that use the spectral basis, the 'order' of each scheme, and the degree of ensemble smoothing), it is found that a particular configuration of the ECO-RAP scheme is best suited to the convective-scale system studied. According to our diagnostics this ECO-RAP configuration still weakens geostrophic and hydrostatic balance, but overall this is less so than for other schemes.

Origin and impact of initialisation shocks in coupled atmosphere-ocean forecasts

Current methods for initialising coupled atmosphere-ocean forecasts often rely on the use of separate atmosphere and ocean analyses, the combination of which can leave the coupled system imbalanced at the beginning of the forecast, potentially accelerating the development of errors. Using a series of experiments with the European Centre for Medium-range Weather Forecasts coupled system, the magnitude and extent of these so-called initialisation shocks is quantified, and their impact on forecast skill measured. It is found that forecasts initialised by separate ocean and atmospheric analyses do exhibit initialisation shocks in lower atmospheric temperature, when compared to forecasts initialised using a coupled data assimilation method. These shocks result in as much as a doubling of root-mean-square error on the first day of the forecast in some regions, and in increases that are sustained for the duration of the 10-day forecasts performed here. However, the impacts of this choice of initialisation on forecast skill, assessed using independent datasets, were found to be negligible, at least over the limited period studied. Larger initialisation shocks are found to follow a change in either the atmospheric or ocean model component between the analysis and forecast phases: changes in the ocean component can lead to sea surface temperature shocks of more than 0.5K in some equatorial regions during the first day of the forecast. Implications for the development of coupled forecast systems, particularly with respect to coupled data assimilation methods, are discussed.

Towards improving the framework for probabilistic forecast evaluation

The evaluation of forecast performance plays a central role both in the interpretation and use of forecast systems and in their development. Different evaluation measures (scores) are available, often quantifying different characteristics of forecast performance. The properties of several proper scores for probabilistic forecast evaluation are contrasted and then used to interpret decadal probability hindcasts of global mean temperature. The Continuous Ranked Probability Score (CRPS), Proper Linear (PL) score, and IJ Good’s logarithmic score (also referred to as Ignorance) are compared; although information from all three may be useful, the logarithmic score has an immediate interpretation and is not insensitive to forecast busts. Neither CRPS nor PL is local; this is shown to produce counter intuitive evaluations by CRPS. Benchmark forecasts from empirical models like Dynamic Climatology place the scores in context. Comparing scores for forecast systems based on physical models (in this case HadCM3, from the CMIP5 decadal archive) against such benchmarks is more informative than internal comparison systems based on similar physical simulation models with each other. It is shown that a forecast system based on HadCM3 out performs Dynamic Climatology in decadal global mean temperature hindcasts; Dynamic Climatology previously outperformed a forecast system based upon HadGEM2 and reasons for these results are suggested. Forecasts of aggregate data (5-year means of global mean temperature) are, of course, narrower than forecasts of annual averages due to the suppression of variance; while the average “distance” between the forecasts and a target may be expected to decrease, little if any discernible improvement in probabilistic skill is achieved.

Long-run restrictions and survey forecasts of output, consumption and investment

We consider the extent to which long-horizon survey forecasts of consumption, investment and output growth are consistent with theory-based steady-state values, and whether imposing these restrictions on long-horizon forecasts will enhance their accuracy. The restrictions we impose are consistent with a two-sector model in which the variables grow at different rates in steady state. The restrictions are imposed by exponential-tilting of simple auxiliary forecast densities. We show that imposing the consumption-output restriction yields modest improvements in the long-horizon output growth forecasts, and larger improvements in the forecasts of the cointegrating combination of consumption and output: the transformation of the data on which accuracy is assessed plays an important role.

Atmospheric and oceanic contributions to irreducible forecast uncertainty of Arctic surface climate

Uncertainty of Arctic seasonal to interannual predictions arising from model errors and initial state uncertainty has been widely discussed in the literature, whereas the irreducible forecast uncertainty (IFU) arising from the chaoticity of the climate system has received less attention. However, IFU provides important insights into the mechanisms through which predictability is lost, and hence can inform prioritization of model development and observations deployment. Here, we characterize how internal oceanic and surface atmospheric heat fluxes contribute to IFU of Arctic sea ice and upper ocean heat content in an Earth system model by analyzing a set of idealized ensemble prediction experiments. We find that atmospheric and oceanic heat flux are often equally important for driving unpredictable Arctic-wide changes in sea ice and surface water temperatures, and hence contribute equally to IFU. Atmospheric surface heat flux tends to dominate Arctic-wide changes for lead times of up to a year, whereas oceanic heat flux tends to dominate regionally and on interannual time scales. There is in general a strong negative covariance between surface heat flux and ocean vertical heat flux at depth, and anomalies of lateral ocean heat transport are wind-driven, which suggests that the unpredictable oceanic heat flux variability is mainly forced by the atmosphere. These results are qualitatively robust across different initial states, but substantial variations in the amplitude of IFU exist. We conclude that both atmospheric variability and the initial state of the upper ocean are key ingredients for predictions of Arctic surface climate on seasonal to interannual time scales.

Enhanced long-range forecast skill in boreal winter following stratospheric strong vortex conditions

There has been a great deal of recent interest in producing weather forecasts on the 2–6 week sub-seasonal timescale, which bridges the gap between medium-range (0–10 day) and seasonal (3–6 month) forecasts. While much of this interest is focused on the potential applications of skilful forecasts on the sub-seasonal range, understanding the potential sources of sub-seasonal forecast skill is a challenging and interesting problem, particularly because of the likely state-dependence of this skill (Hudson et al 2011). One such potential source of state-dependent skill for the Northern Hemisphere in winter is the occurrence of stratospheric sudden warming (SSW) events (Sigmond et al 2013). Here we show, by analysing a set of sub-seasonal hindcasts, that there is enhanced predictability of surface circulation not only when the stratospheric vortex is anomalously weak following SSWs but also when the vortex is extremely strong. Sub-seasonal forecasts initialized during strong vortex events are able to successfully capture the associated surface temperature and circulation anomalies. This results in an enhancement of Northern annular mode forecast skill compared to forecasts initialized during the cases when the stratospheric state is close to climatology. We demonstrate that the enhancement of skill for forecasts initialized during periods of strong vortex conditions is comparable to that achieved for forecasts initialized during weak events. This result indicates that additional confidence can be placed in sub-seasonal forecasts when the stratospheric polar vortex is significantly disturbed from its normal state.

The EarthCARE satellite: the next step forward in global measurements of clouds, aerosols, precipitation, and radiation

The collective representation within global models of aerosol, cloud, precipitation, and their radiative properties remains unsatisfactory. They constitute the largest source of uncertainty in predictions of climatic change and hamper the ability of numerical weather prediction models to forecast high-impact weather events. The joint European Space Agency (ESA)–Japan Aerospace Exploration Agency (JAXA) Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) satellite mission, scheduled for launch in 2018, will help to resolve these weaknesses by providing global profiles of cloud, aerosol, precipitation, and associated radiative properties inferred from a combination of measurements made by its collocated active and passive sensors. EarthCARE will improve our understanding of cloud and aerosol processes by extending the invaluable dataset acquired by the A-Train satellites CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Aqua. Specifically, EarthCARE’s cloud profiling radar, with 7 dB more sensitivity than CloudSat, will detect more thin clouds and its Doppler capability will provide novel information on convection, precipitating ice particle, and raindrop fall speeds. EarthCARE’s 355-nm high-spectral-resolution lidar will measure directly and accurately cloud and aerosol extinction and optical depth. Combining this with backscatter and polarization information should lead to an unprecedented ability to identify aerosol type. The multispectral imager will provide a context for, and the ability to construct, the cloud and aerosol distribution in 3D domains around the narrow 2D retrieved cross section. The consistency of the retrievals will be assessed to within a target of ±10 W m–2 on the (10 km)2 scale by comparing the multiview broadband radiometer observations to the top-of-atmosphere fluxes estimated by 3D radiative transfer models acting on retrieved 3D domains.