Showing papers in "Quarterly Journal of the Royal Meteorological Society in 2016"

A review on Arctic sea‐ice predictability and prediction on seasonal to decadal time‐scales

Abstract: Sea ice plays a crucial role in the Earth's energy and water budget and has a substantial impact on local and remote atmospheric and oceanic circulations. Predictions of Arctic sea-ice conditions a few months to a few years in advance could be of interest for stakeholders. This article presents a review of the potential sources of Arctic sea-ice predictability on these time-scales. Predictability mainly originates from persistence or advection of sea-ice anomalies, interactions with the ocean and atmosphere and changes in radiative forcing. After estimating the inherent potential predictability limit with state-of-the-art models, current sea-ice forecast systems are described, together with their performance. Finally, some challenges and issues in sea-ice forecasting are presented, along with suggestions for future research priorities.

A coupled data assimilation system for climate reanalysis

Abstract: A coupled data assimilation system has been developed at the European Centre for Medium-Range Weather Forecasts (ECMWF), which is intended to be used for the production of global reanalyses of the recent climate. The system assimilates a wide variety of ocean and atmospheric observations and produces ocean–atmosphere analyses with a coupled model. Employing the coupled-model constraint in the analysis implies that assimilation of an ocean observation has immediate impact on the atmospheric state estimate and, conversely, assimilation of an atmospheric observation affects the ocean state. This covariance between atmosphere and ocean induced by the analysis method is illustrated with simple numerical experiments. Realistic data assimilation experiments based on the global observing system are then used to assess the quality of the assimilation method. Comparison with an uncoupled system shows a mostly neutral impact overall, with slightly improved temperature estimates in the upper ocean and lower atmosphere. These preliminary results are considered of interest for the ongoing community efforts focusing on coupled data assimilations.

Kilometre-scale ensemble data assimilation for the COSMO model (KENDA)

Abstract: An ensemble Kalman filter for convective-scale data assimilation (KENDA) has been developed for the COnsortium for Small-scale MOdelling (COSMO) model. The KENDA system comprises a local ensemble transform Kalman filter (LETKF) and a deterministic analysis based on the Kalman gain for the analysis ensemble mean. The KENDA software suite includes tools for adaptive localization, multiplicative covariance inflation, relaxation to prior perturbations and adaptive observation errors. In the version introduced here, conventional data (radiosonde, aircraft, wind profiler, surface station data) are assimilated. Latent heat nudging of radar precipitation has also been added to the KENDA system to be applied to the deterministic analysis only or additionally to all ensemble members. The performance of different system components is investigated in a quasi-operational setting using a basic cycling environment (BACY) for a period of six days with 24 h forecasts. For this period and an additional 28 day period, deterministic KENDA forecasts are compared with forecasts based on the observation nudging data assimilation scheme, which is currently operational at the German Weather Service (Deutscher Wetterdienst, DWD). For our experiments, lateral boundary conditions for the regional model are given by a global ensemble Kalman filter for the ICOsahedral Nonhydrostatic (ICON) model. The performance of the KENDA system proves overall to be superior to the forecast quality of the operational nudging scheme, in particular with regard to precipitation. Latent heat nudging improves precipitation forecasts in both systems and has slightly more benefit in combination with the LETKF than with observation nudging.

Self-aggregation of convection in long channel geometry

Abstract: Cloud cover and relative humidity in the Tropics are strongly influenced by organized atmospheric convection, which occurs across a range of spatial and temporal scales. One mode of organization that is found in idealized numerical modelling simulations is self-aggregation, a spontaneous transition from randomly distributed convection to organized convection despite homogeneous boundary conditions. We explore the influence of domain geometry on the mechanisms, growth rates and length-scales of self-aggregation of tropical convection. We simulate radiative–convective equilibrium with the System for Atmospheric Modeling (SAM), in a non-rotating, highly elongated three-dimensional (3D) channel domain of length >104 km, with interactive radiation and surface fluxes and fixed sea-surface temperature varying from 280–310 K. Convection self-aggregates into multiple moist and dry bands across this full range of temperatures. As convection aggregates, we find a decrease in upper tropospheric cloud fraction but an increase in lower tropospheric cloud fraction; this sensitivity of clouds to aggregation agrees with observations in the upper troposphere but not in the lower troposphere. An advantage of the channel geometry is that a separation distance between convectively active regions can be defined; we present a theory for this distance based on boundary layer. We find that surface fluxes and radiative heating act as positive feedback mechanisms, favouring self-aggregation, but advection of moist static energy acts as a negative feedback, opposing self-aggregation, for nearly all temperatures and times. Early in the process of self-aggregation, surface fluxes are a positive feedback at all temperatures, shortwave radiation is a strong positive feedback at low surface temperatures but weakens at higher temperatures and longwave radiation is a negative feedback at low temperatures but becomes a positive feedback for temperatures greater than 295–300 K. Clouds contribute strongly to the radiative feedback, especially at low temperatures.

A comparison of the regional Arctic System Reanalysis and the global ERA-Interim Reanalysis for the Arctic

Abstract: The Arctic System Reanalysis version 1 (ASRv1), a high-resolution regional assimilation of model output, observations and satellite data across the mid- and high latitudes of the Northern Hemisphere, and the global European Centre for Medium Range Forecasting Interim Reanalysis (ERAI) are compared with atmospheric observations for the period December 2006 to November 2007. Results throughout the troposphere show observations to be well assimilated in the ASRv1, as monthly and annual near-surface (upper-level) temperature, dew-point (relative humidity), pressure (geopotential height) and wind-speed biases compared with surface stations and radiosondes are very small. These results are similar to the ERAI, although wind-speed biases are significantly smaller in the ASRv1. Despite the ASRv1's use of a 3D-variational (Var) assimilation compared with the ERAI's 4D-Var, similar results suggest that a regional approach with higher-resolution terrain and a detailed land-surface description forced by a global reanalysis may improve the assimilation of observations and help offset temporal information lost by the 3D-Var compared with the 4D-Var. However, the ASRv1 forecast field results compared with the ERAI are mixed. The ASRv1 and ERAI show negative precipitation biases during cool months compared with gauge observations, and too much precipitation falls in the ASRv1 during summer in the midlatitudes. Stations north of 60°N demonstrate smaller precipitation biases in the ASRv1 than the ERAI except during the summer, when the ASRv1 is very dry. Short-wave radiation compared with observations is much too large in the ASRv1, and both reanalyses show long-wave radiation deficits during most months. These results point to inadequacies in model physics in the ASRv1 (e.g. convective and radiation schemes) that will continue to be refined in subsequent versions of the ASR.

The evolution of the ECMWF hybrid data assimilation system

Abstract: The trend towards using flow-dependent, ensemble-based estimates of background-error covariances has been one of the main themes of atmospheric data assimilation research and development in recent years. In this work it is documented how flow-dependent ensemble information from the ECMWF ensemble of data assimilations (EDA) has gradually been incorporated into the B model which describes the background-error covariance matrix at the start of the ECMWF 4D-Var assimilation window. Starting with background-error variances for the balanced part of the control vector and observation quality control, the current article extends the flow-dependency to background-error variances for the unbalanced part of the control vector and for background-error correlation structures. The correlations are determined either online from previous days or from a hybrid of climatological and current cycle estimates. Each of these changes is shown to improve both the realism of the modelled B and the accuracy of the analysis and forecast fields produced by the 4D-Var assimilation cycle which makes use of the improved B. Finally, increasing the resolution at which the EDA 4D-Vars are run is shown to reduce the underestimation of the EDA-based error estimates.

Seasonal to decadal prediction of the winter North Atlantic Oscillation: emerging capability and future prospects

Abstract: European and North American winter weather is dominated by year-to-year variations in the North Atlantic Oscillation (NAO) which controls the direction and speed of the prevailing winds. An ability to forecast the time-averaged NAO months to years ahead would be of great societal benefit, but current operational seasonal forecasts show little skill. However, there are several elements of the climate system that potentially influence the NAO and may therefore provide predictability for the NAO. We review these potential sources of skill, present emerging evidence that the NAO may be usefully predictable (with correlations exceeding 0.6) on seasonal time-scales, and discuss prospects for improving skill and extending predictions to multi-year time-scales.

Arctic amplification enhanced by latent energy transport of atmospheric planetary waves

Abstract: The atmospheric northward energy transport plays a crucial role for the Arctic climate; this transport brings to the Arctic an amount of energy comparable to that provided directly by the sun. The transport is accomplished by atmospheric waves–for instance large-scale planetary waves and meso-scale cyclones–and the zonal-mean circulation. These different components of the energy transport impact the Arctic climate differently. A split of the transport into stationary and transient waves constitutes a traditional way of decomposing the transport. However this procedure does not take into account the transport accomplished separately by the planetary and synoptic-scale waves. Here a Fourier decomposition is applied, which decomposes the transport with respect to zonal wave numbers. Reanalysis and model data reveal that the planetary waves impact Arctic temperatures much more than do synoptic-scale waves. In addition the latent transport by these waves affects the Arctic climate more than does the dry-static part. Finally, the EC-Earth model suggests that changes of the energy transport over the twentyfirst century will contribute to Arctic warming, despite the fact that in this model the total energy transport to the Arctic will decrease. This apparent contradictory result is due to the cooling induced by a decrease of the dry-static transport by planetary waves being more than compensated for by a warming caused by the latent counterpart.

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

Abstract: 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 Nino-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 Nino and the QBO compared to low-top models. Enhanced conditional wintertime skill over high-latitudes and the North Atlantic region during winters with El Nino conditions suggests a possible role for a stratospheric pathway.

Sea ice forecast verification in the Canadian Global Ice Ocean Prediction System

Abstract: Recent increases in marine traffic in the Arctic have amplified the demand for reliable ice and marine environmental predictions. This article presents the verification of ice forecast skill from a new system implemented recently at the Canadian Meteorological Centre called the Global Ice Ocean Prediction System (GIOPS). GIOPS provides daily global ice and ocean analyses and 10-day forecasts on a 1/4°-resolution grid. GIOPS includes a multivariate ocean data assimilation system that combines satellite observations of sea-level anomaly and sea-surface temperature (SST) together with in situ observations of temperature and salinity. Ice analyses are produced using a 3D-Var method that assimilates satellite observations from SSM/I and SSMIS together with manual analyses from the Canadian Ice Service. Analyses of total ice concentration are projected onto the thickness categories used in the ice model using spatially and temporally varying weighting functions derived from ice model tendencies. This method may reduce deleterious impacts on the ice thickness distribution when assimilating ice concentration, as it can directly modulate (and reverse) nonlinear processes such as ice deformation. An objective verification of sea ice forecasts is made using two methods: analysis-based error assessment focusing on the marginal ice zone, and a contingency table approach to evaluate ice extent as compared to an independent analysis. Together the methods demonstrate a consistent picture of skilful medium-range forecasts in both the Northern and Southern Hemispheres as compared to persistence. Using the contingency table approach, GIOPS forecasts show a significant open-water bias during spring and summer. However, this bias depends on the choice of threshold used. Ice forecast skill is found to be highly sensitive to the assimilation of SST near the ice edge. Improved observational coverage in these areas (including salinity) would be extremely valuable for further improvement in ice forecast skill.

Eleven-year solar cycle signal in the NAO and Atlantic/European blocking

Abstract: The 11-year solar cycle signal in December–January–February (DJF) averaged mean-sea-level pressure (SLP) and Atlantic/European blocking frequency is examined using multilinear regression with indices to represent variability associated with the solar cycle, volcanic eruptions, the El Nino–Southern Oscillation (ENSO) and the Atlantic Multidecadal Oscillation (AMO). Results from a previous 11-year solar cycle signal study of the period 1870–2010 (140 years; ∼13 solar cycles) that suggested a 3–4 year lagged signal in SLP over the Atlantic are confirmed by analysis of a much longer reconstructed dataset for the period 1660–2010 (350 years; ∼32 solar cycles). Apparent discrepancies between earlier studies are resolved and stem primarily from the lagged nature of the response and differences between early- and late-winter responses. Analysis of the separate winter months provide supporting evidence for two mechanisms of influence, one operating via the atmosphere that maximises in late winter at 0–2 year lags and one via the mixed-layer ocean that maximises in early winter at 3–4 year lags. Corresponding analysis of DJF-averaged Atlantic/European blocking frequency shows a highly statistically significant signal at ∼1-year lag that originates primarily from the late winter response. The 11-year solar signal in DJF blocking frequency is compared with other known influences from ENSO and the AMO and found to be as large in amplitude and have a larger region of statistical significance.

Spatial and temporal distribution of hailstorms in the Alpine region: a long‐term, high resolution, radar‐based analysis

Abstract: This article presents a 13-year hail climatology for Switzerland based on volumetric radar reflectivity. Two radar-based hail detection products that are used operationally at MeteoSwiss, namely the Probability of Hail (POH) and the Maximum Expected Severe Hail Size (MESHS), have been reprocessed for the extended convective season (April–September) between 2002 and 2014. The result of these two products is a comprehensive hail distribution map, which highlights regional and local-scale hail characteristics. The map of the annual number of hail days shows a high spatial variability and several maxima over the foothills north and south of the Alps as wells as over the Jura mountains. Directly over the Alps hail frequency exhibits a minimum. Annual hail anomalies show a pronounced variability, which suggests that hail occurrence is strongly controlled by large-scale weather patterns. Furthermore, hail probability exhibits a strong seasonal and diurnal cycle with a maximum in July in the late afternoon. The hail peak over the northern pre-alpine region occurs approximately two hours earlier compared to the south. A possible explanation is the trigger mechanism between the cold pool initiated by early convective cells over the Jura mountains and the development of cells on the northern slope of the Alps. Since radar-based hail signals are only indirect measurements, statistical verification of the hail detection algorithms is crucial. Damage reports from an automobile insurance company are used as an independent dataset. Verification results confirm that radar-based hail algorithms provide valuable information on hail probability. Finally, the synoptic-scale hail-driving weather conditions are investigated using a weather type classification based on upper-air flow direction and mean pressure from a numerical weather prediction model. The results show that six out of nine main synoptic-scale patterns favour the development of hailstorms in Switzerland.

The London Model: forecasting fog at 333 m resolution

Abstract: A very high resolution numerical weather prediction model is nested inside the Met Office's main United Kingdom forecast model to investigate whether further enhancements to resolution provide any benefit for fog forecasting. The London Model shows similar performance to its lower resolution equivalent at short lead times, but improved performance at longer lead times and an improved frequency bias of forecast fog events. Differences in the model cloud parametrization are the key reason for the differing behaviour, leading to systematically less cloud, colder night time minimum temperatures and therefore more fog in the London Model. Benefit of the enhanced resolution is also found, via an improved representation of how orographic variability enhances turbulence in the stable boundary layer.

Theoretical insight into diagnosing observation error correlations using observation-minus-background and observation-minus-analysis statistics

Abstract: To improve the quantity and impact of observations used in data assimilation it is necessary to take into account the full, potentially correlated, observation error statistics. A number of methods for estimating correlated observation errors exist, but a popular method is a diagnostic that makes use of statistical averages of observation-minus-background and observation-minus-analysis residuals. The accuracy of the results it yields is unknown as the diagnostic is sensitive to the difference between the exact background and exact observation error covariances and those that are chosen for use within the assimilation. It has often been stated in the literature that the results using this diagnostic are only valid when the background and observation error correlation length scales are well separated. Here we develop new theory relating to the diagnostic. For observations on a 1D periodic domain we are able to the show the effect of changes in the assumed error statistics used in the assimilation on the estimated observation error covariance matrix. We also provide bounds for the estimated observation error variance and eigenvalues of the estimated observation error correlation matrix. We demonstrate that it is still possible to obtain useful results from the diagnostic when the background and observation error length scales are similar. In general, our results suggest that when correlated observation errors are treated as uncorrelated in the assimilation, the diagnostic will underestimate the correlation length scale. We support our theoretical results with simple illustrative examples. These results have potential use for interpreting the derived covariances estimated using an operational system.

Forecasting radiation fog at climatologically contrasting sites: evaluation of statistical methods and WRF

Abstract: A 6-year climatology of radiation fog has been compiled at two sites: the Research Centre for the Lower Atmosphere (CIBA, Spain) and the Cabauw Experimental Site for Atmospheric Research (CESAR, The Netherlands). These sites are contrasting in terms of geographical situation, climate zone, altitude, humidity and soil water availability. Therefore, several climatological differences in fog abundance, onset, dissipation and duration have been quantified between both sites. The more humid site (CESAR) is characterised by relatively short radiation-fog events distributed throughout the year. However, radiation fog at the drier site (CIBA) is more persistent and appears during late-autumn/winter months. In general, its formation requires more time after sunset (~ 2 h more), since further cooling is required to reach saturation. The forecast of these fog events has been evaluated through two different approaches. On the one hand, we extend the statistical method presented by [Menut ~al.(2014)Menut, Mailler, Dupont, Haeffelin and Elias] (M14). This method uses statistics to define threshold values on key variables for fog formation (pre-fog) and verifies its predictability using observations and numerical model output. We present some of the most appropriate threshold values for the forecasting of pre-fog periods at both sites, which differ from those presented in M14 and depend on the optimisation of the hit-rate or the false-alarm rate. Additionally, we also extend M14 suggesting other variables as potential predictors for fog formation (friction velocity and visibility tendency). Finally, we focus on the fog simulation by the Weather Research and Forecasting (WRF) model in terms of liquid water content. The WRF model was able to simulate radiation fog when configured with sophisticated physical options and high resolution. However it failed simulating the onset, dissipation and vertical extension of fog (overestimated). The model results were extremely sensitive to the spin-up time.

Assimilation of 3D Radar Reflectivities with an Ensemble Kalman Filter on the Convective Scale

Abstract: An ensemble data assimilation system for 3D radar reflectivity data is introduced for the convection-permitting numerical weather prediction model of the COnsortium for Small-scale MOdelling (COSMO) based on the Kilometre-scale ENsemble Data Assimilation system (KENDA), developed by Deutscher Wetterdienst and its partners. KENDA provides a state-of-the-art ensemble data assimilation system on the convective scale for operational data assimilation and forecasting based on the Local Ensemble Transform Kalman Filter (LETKF). In this study, the Efficient Modular VOlume RADar Operator is applied for the assimilation of radar reflectivity data to improve short-term predictions of precipitation. Both deterministic and ensemble forecasts have been carried out. A case-study shows that the assimilation of 3D radar reflectivity data clearly improves precipitation location in the analysis and significantly improves forecasts for lead times up to 4 h, as quantified by the Brier Score and the Continuous Ranked Probability Score. The influence of different update rates on the noise in terms of surface pressure tendencies and on the forecast quality in general is investigated. The results suggest that, while high update rates produce better analyses, forecasts with lead times of above 1 h benefit from less frequent updates. For a period of seven consecutive days, assimilation of radar reflectivity based on the LETKF is compared to that of DWD's current operational radar assimilation scheme based on latent heat nudging (LHN). It is found that the LETKF competes with LHN, although it is still in an experimental phase.

Predictability of tropical cyclone intensity: scale‐dependent forecast error growth in high‐resolution stochastic kinetic‐energy backscatter ensembles

Abstract: A systematic study of the intrinsic predictability of tropical cyclone (TC) intensity is conducted using a set of cloud-resolving model ensembles of Hurricane Earl (2010). The ensembles are perturbed with a stochastic kinetic-energy backscatter scheme (SKEBS) and started from identical initial conditions. Scale-dependent error growth is investigated by imposing stochastic perturbations with various spatial scales on the TC and its environment. Predictability limits (upper bound) are determined by computing the error magnitude associated with each component of the Fourier-decomposed TC wind fields at forecast times up to 7 days. Three SKEBS ensembles with different perturbation scales are used to investigate the effects of small-scale, mesoscale and large-scale uncertainties on the predictability of TC intensity. In addition, the influence of the environmental flow is investigated by perturbing the lateral boundary conditions. It is found that forecast errors grow rapidly on small scales (azimuthal wave numbers > 20), which saturate within 6–12 h in all four ensembles, regardless of perturbation scale. Errors grow relatively slower on scales corresponding to rain bands (wave numbers 2–5), limiting the predictability of these features to 1–5 days. Earl's mean vortex and the wave number-1 asymmetry are comparatively resistant to error growth and remain predictable for at least 7 days. Forecast uncertainty of the mean vortex and wave number-1 asymmetry is greater in the large-scale perturbation and perturbed lateral boundary condition ensembles. The results from this case indicate that the predictability of the mean vortex and wave number-1 asymmetry is predominately associated with the predictability of the large-scale environment, which is generally much longer than that of convective-scale processes within the TC.

The efficiency of diabatic heating and tropical cyclone intensification

Abstract: Widely held arguments attributing the increasingly rapid intensification of tropical cyclones to the increasing efficiency' of diabatic heating in the cyclone's inner core region associated with deep convection are examined. The efficiency, in essence the amount of temperature warming compared with the amount of latent heat released, is argued to increase as the vortex strengthens on account of the strengthening inertial stability. Another aspect of the efficiency ideas concerns the location of the heating in relation to the radius of maximum tangential wind speed, with heating inside this radius seen to be more efficient in rapidly developing a warm-core thermal structure and, presumably, a rapid increase in the tangential wind. A more direct interpretation of the increased spin-up rate is offered when the diabatic heating is located inside the radius of maximum tangential wind speed. Further, we draw attention to the limitations of assuming a fixed diabatic heating rate as the vortex intensifies and offer reasons, on these grounds alone, as to why it is questionable to apply the efficiency argument to interpret the results of observations or numerical model simulations of tropical cyclones. Moreover, since the spin-up of the maximum tangential winds in a tropical cyclone takes place in the boundary layer and the spin-up of the eyewall is a result of the vertical advection of high angular momentum from the boundary layer, it is questionable also whether deductions about efficiency in theories that neglect the boundary-layer dynamics and thermodynamics are relevant to reality.

Assessment of summer monsoon precipitation derived from five reanalysis datasets over East Asia

Abstract: Precipitation, which is the predominant component of the East Asian summer monsoon (EASM), may have large uncertainties among reanalysis datasets. We comprehensively evaluate the performance of five reanalysis datasets in reproducing the EASM precipitation. These datasets are NCEP/NCAR Reanalysis 1 project (NCEP1), NCEP/US Department of Energy Atmospheric Model Intercomparison Project II reanalysis (NCEP2), Japanese 25-year Reanalysis project (JRA-25), Interim ECMWF Reanalysis (ERA-Interim), and Modern Era Retrospective-analysis for Research and Applications (MERRA). Results show that the five reanalysis datasets can generally reproduce the climatology and interannual variability of EASM precipitation. Especially, MERRA and ERA-Interim have the highest skills. Considering different-class precipitation, large uncertainties exist in the category of non-rainfall and heavy rainfall. The five reanalysis datasets overestimate the non-rainfall frequency, and JRA-25 and NCEP2 overestimate the heavy rainfall frequency. The well-known interdecadal variation around the mid-1990s can also be better depicted by ERA-Interim and MERRA. For the linear trend of precipitation, only MERRA can reasonably reproduce the increasing tendency over southern China and the western Pacific and the decreasing tendency over the Indo-China Peninsula. Based on EOF analysis, the spatial–temporal structure of EASM precipitation has been examined. MERRA, NCEP1 and ERA-Interim can better capture both the spatial patterns and principle components of the first two EOF modes. Based on our evaluation, the preferential reanalysis datasets for investigating the EASM precipitation are ERA-Interim and MERRA, which also permit the more precise investigation of interannual to decadal variability.

Foehn warming distributions in nonlinear and linear flow regimes: a focus on the Antarctic Peninsula

Abstract: The structure of lee-side warming during foehn events is investigated as a function of cross-barrier flow regime linearity. Two contrasting cases of westerly flow over the Antarctic Peninsula (AP) are considered – one highly nonlinear, the other relatively linear. Westerly flow impinging on the AP provides one of the best natural laboratories in the world for the study of foehn, owing to its maritime setting and the Larsen C Ice Shelf (LCIS) providing an expansive, homogeneous and smooth surface on its east side. Numerical simulations with the Met Office Unified Model (at 1.5 km grid size) and aircraft observations are utilized. In case A, relatively weak southwesterly cross-Peninsula flow and an elevated upwind inversion dictate a highly nonlinear foehn event, with mountain wave breaking observed. The consequent strongly accelerated downslope flow leads to high-amplitude warming and ice-shelf melt in the immediate lee of the AP. However this foehn warming diminishes rapidly downwind due to upward ascent of the foehn flow via a hydraulic jump. In case C, strong northwesterly winds dictate a relatively linear flow regime. There is no hydraulic jump and strong foehn winds are able to flow at low levels across the entire ice shelf, mechanically mixing the near-surface flow, preventing the development of a strong surface inversion and delivering large fluxes of sensible heat to the ice shelf. Consequently, in case C ice-melt rates are considerably greater over the LCIS as a whole than in case A. Our results imply that although nonlinear foehn events cause intense warming in the immediate lee of mountains, linear foehn events will commonly cause more extensive lee-side warming and, over an ice surface, higher melt rates. This has major implications for the AP, where recent east-coast warming has led to the collapse of two ice shelves immediately north of the LCIS.

Evaluation of the CMCC eddy‐permitting global ocean physical reanalysis system (C‐GLORS, 1982–2012) and its assimilation components

Abstract: Ocean reanalyses are data assimilative simulations, aimed at estimating the four-dimensional state of the ocean over long periods, in a way as consistent over time as possible. They are designed for a wide range of climate applications, such as climate monitoring and low-frequency variability studies, along with several downstream applications (e.g. biogeochemical and fishery modelling, initial conditions for long-range coupled predictions, regional model nesting). An upgraded version of the Euro-Mediterranean Center for Climate Change (CMCC) eddy-permitting global ocean reanalysis, named CMCC Global Ocean Reanalysis System (C-GLORS) version 4, was recently released. The reanalysis covers the meteorological satellite era (1982–2012). This article details the configuration of the reanalysis system and provides an extensive validation, focusing on the evaluation of main indexes related to climate monitoring. Cumulative denial experiments are also conducted, in order to understand the relative impact of assimilation components included in C-GLORS (i.e. altimetric data, variational assimilation, bias correction and surface nudging). Results indicate that C-GLORS proves reliable in simulating long-term means, heat and freshwater trends, sea-level variability, mean surface circulation and transports, eddy variability and meridional overturning circulation and its associated heat transport, except for a few specific issues (overestimation of volume transports in the Southern Ocean and slight underestimation of the Atlantic ocean meridional overturning circulation and associated heat transport, the latter mostly linked to underestimation of western boundary northward transports). The results also demonstrate the complementarity of the assimilation components, all improving verification skill scores, for example the importance of the variational assimilation for the simulation of the reanalysis small-scale variability, the importance of the bias-correction scheme for correcting subsurface salinity errors or the role of surface nudging in driving the North Atlantic ocean circulation. The analyses presented here offer ideas for improving C-GLORS further and for the requirements of next-generation ocean reanalysis systems.

Maintaining Atmospheric Mass and Water Balance in Reanalyses

Abstract: This report describes the modifications implemented into the Goddard Earth Observing System Version-5 (GEOS-5) Atmospheric Data Assimilation System (ADAS) to maintain global conservation of dry atmospheric mass as well as to preserve the model balance of globally integrated precipitation and surface evaporation during reanalysis. Section 1 begins with a review of these global quantities from four current reanalysis efforts. Section 2 introduces the modifications necessary to preserve these constraints within the atmospheric general circulation model (AGCM), the Gridpoint Statistical Interpolation (GSI) analysis procedure, and the Incremental Analysis Update (IAU) algorithm. Section 3 presents experiments quantifying the impact of the new procedure. Section 4 shows preliminary results from its use within the GMAO MERRA-2 Reanalysis project. Section 5 concludes with a summary.

Impact of climate change on urban heat island effect and extreme temperatures: a case-study

Abstract: This study investigated the impacts of climate change on the urban heat island (UHI) and the number of very hot (maximum temperature >35°C) and very cold days (minimum temperature <5°C) in the central business district (CBD) of Melbourne city in Australia. A station located in Laverton (less urbanised area), which is 17 km southwest of Melbourne CBD, was selected as the reference station for the computation of UHI intensity in Melbourne CBD. Using daily minimum/maximum temperatures at the two stations, nocturnal/diurnal UHI intensities in Melbourne CBD were computed for the period 1952–2010. It was found that in Melbourne CBD, nocturnal UHI intensities show a clear rising trend over the period 1952–2010 unlike the diurnal UHI intensities. For the analysis of nocturnal UHI intensities in Melbourne CBD, under changing climate, for each calendar month statistical models based on the gene expression programming (GEP) technique were developed for downscaling general-circulation model (GCM) outputs to monthly average minimum temperature at Melbourne CBD and Laverton. Using the outputs of HadCM3, GFDL2.0 and ECHAM5 pertaining to the A2 greenhouse gas emission scenario on the downscaling models, projections of monthly average minimum temperature were produced for the two stations over the period 2000–2099. In each season, at both stations, the ensemble average of monthly minimum temperature gradually increased over the period 2000–2099. The ensemble-average UHI intensity in Melbourne CBD projected into the future was higher for all seasons in comparison to that of period 1952–1971. Downscaling models based on the GEP technique were developed for each calendar month for projecting the number of very hot days in November–March and very cold days in May–September in Melbourne CBD. It was found that, in the future, summer weather will spread to early autumn, and winter weather will move to early spring, in Melbourne CBD.

Improved stochastic physics schemes for global weather and climate models

Abstract: The importance of probabilistic weather predictions and climate projections is growing. One of the key elements of the former is stochastic physics, schemes that perturb some uncertain processes in a general circulation model (GCM), such as physical parametrizations or diffusion. They help to increase the ensemble dispersion of ensemble prediction systems (EPS) and in some cases improve certain atmospheric processes by noise-induced drifts. We have developed a new configuration of stochastic physics schemes for the Met Office Unified Model (MetUM). It consists of an improved Stochastic Kinetic Energy Backscatter v2 (SKEB2), plus the Stochastic Perturbation of Tendencies (SPT). The improvements to SKEB2 remove spurious physical artefacts, e.g. a spurious wave caused by low-wave-number perturbations, and improve the resolution sensitivity of the scheme. The SPT produces a larger ensemble spread in the Tropics than present schemes, but its impact on long-term climate budgets makes the use of conservation constraints for water vapour and energy essential. The new configuration produces a higher impact in the Tropics, increasing the ensemble spread and improving some long-standing climate biases in areas of excessive convection, whilst minimizing the negative impact on tropical processes like tropical convective waves.

Sensitivity of the AROME ensemble to initial and surface perturbations during HyMeX

Abstract: The AROME-EPS convection-permitting ensemble prediction system has been evaluated over the HyMeX-SOP1 period. Objective verification scores are computed using dense observing networks prepared for the HyMeX experiment. In probabilistic terms, the AROME-EPS ensemble performs better than the AROME-France deterministic prediction system, and a state-of-the-art ensemble at a lower resolution. The strengths and weaknesses of AROME-EPS are discussed. Here, impact exper- iments are used to study perturbation schemes for the initial conditions and the model surface. Both have a significant effect on the ensemble performance. The interactions between the perturbations of lateral boundaries, initial conditions and surface perturbations are studied. The consistency between initial and lateral perturbations is found to be unimportant from a meteorological point of view. En- semble data assimilation is not as effective as a simpler surface perturbation scheme, and it is noted that both approaches could be usefully combined.

Performance of 4D‐Var NWP‐based nowcasting of precipitation at the Met Office for summer 2012

Abstract: The Met Office developed and demonstrated an hourly cycling 1.5 km resolution NWP-based nowcast system (0–6 hour forecasts) using 4-dimensional variational data assimilation (4D-Var). This was known as the Nowcasting Demonstration Project (NDP), and was principally for prediction of convective storms for flood forecasting. The NDP was run in real-time from March 2012 to April 2013 to cover the period of the London Olympics 2012. The system was run on a domain covering southern England and Wales nested in the UK variable resolution model (UKV). The UKV used a UK-wide 1.5 km domain with 3-hourly cycling 3-dimensional variational data assimilation (3D-Var) and produced 36 hour forecasts every 6 hours. The NDP 4D-Var included standard observations, Doppler radar radial winds, humidity derived from a 3D cloud cover analysis and geostationary satellite upper-tropospheric water vapour radiances not contaminated by cloud. This was used in combination with latent heat nudging of radar-derived precipitation rates. Example case studies compare the NDP precipitation forecasts to both the operational extrapolation/merged nowcast system and the UKV forecasts. Objective comparison of fraction skill score for the period June to August 2012 shows that the NDP skill was greater than the latest UKV forecasts, available to forecasters at the same time as the NDP, for the whole 6 hour forecast period. The skill of the NDP was greater than the operational extrapolation/merged nowcast beyond T+2 hours.