The surface temperatures of Earth: steps towards integrated understanding of variability and change

Surface temperature is a key aspect of weather and climate, but the term may refer to different quantities that play interconnected roles and are observed by different means. In a community-based activity in June 2012, the EarthTemp Network brought together 55 researchers from five continents to improve the interaction between scientific communities who focus on surface temperature in particular domains, to exploit the strengths of different observing systems and to better meet the needs of different communities. The workshop identified key needs for progress towards meeting scientific and societal requirements for surface temperature understanding and information, which are presented in this community paper. A "whole-Earth" perspective is required with more integrated, collaborative approaches to observing and understanding Earth's various surface temperatures. It is necessary to build understanding of the relationships between different surface temperatures, where presently inadequate, and undertake large-scale systematic intercomparisons. Datasets need to be easier to obtain and exploit for a wide constituency of users, with the differences and complementarities communicated in readily understood terms, and realistic and consistent uncertainty information provided. Steps were also recommended to curate and make available data that are presently inaccessible, develop new observing systems and build capacities to accelerate progress in the accuracy and usability of surface temperature datasets.

Propagation of the Madden-Julian Oscillation through the Maritime Continent and scale interaction with the diurnal cycle of precipitation

The convectively active part of the Madden-Julian Oscillation (MJO) propagates eastward through the warm pool, from the Indian Ocean through the Maritime Continent (the Indonesian archipelago) to the western Pacific. The Maritime Continent's complex topography means the exact nature of the MJO propagation through this region is unclear. Model simulations of the MJO are often poor over the region, leading to local errors in latent heat release and global errors in medium-range weather prediction and climate simulation. Using 14 northern winters of TRMM satellite data it is shown that, where the mean diurnal cycle of precipitation is strong, 80% of the MJO precipitation signal in the Maritime Continent is accounted for by changes in the amplitude of the diurnal cycle. Additionally, the relationship between outgoing long-wave radiation (OLR) and precipitation is weakened here, such that OLR is no longer a reliable proxy for precipitation. The canonical view of the MJO as the smooth eastward propagation of a large-scale precipitation envelope also breaks down over the islands of the Maritime Continent. Instead, a vanguard of precipitation (anomalies of 2.5 mm day^-1 over 10^6 km^2) jumps ahead of the main body by approximately 6 days or 2000 km. Hence, there can be enhanced precipitation over Sumatra, Borneo or New Guinea when the large-scale MJO envelope over the surrounding ocean is one of suppressed precipitation. This behaviour can be accommodated into existing MJO theories. Frictional and topographic moisture convergence and relatively clear skies ahead of the main convective envelope combine with the low thermal inertia of the islands, to allow a rapid response in the diurnal cycle which rectifies onto the lower-frequency MJO. Hence, accurate representations of the diurnal cycle and its scale interaction appear to be necessary for models to simulate the MJO successfully.

The Madden-Julian Oscillation and the diurnal cycle over the Maritime Continent: scale interactions and modelling

The Maritime Continent archipelago, situated on the equator at 95-165E, has the strongest land-based precipitation on Earth. The latent heat release associated with the rainfall affects the atmospheric circulation throughout the tropics and into the extra-tropics. The greatest source of variability in precipitation is the diurnal cycle. The archipelago is within the convective region of the Madden-Julian Oscillation (MJO), which provides the greatest variability on intra-seasonal time scales: large-scale (∼10^7 km^2) active and suppressed convective envelopes propagate slowly (∼5 m s^-1) eastwards between the Indian and Pacific Oceans. High-resolution satellite data show that a strong diurnal cycle is triggered to the east of the advancing MJO envelope, leading the active MJO by one-eighth of an MJO cycle (∼6 days). Where the diurnal cycle is strong its modulation accounts for 81% of the variability in MJO precipitation. Over land this determines the structure of the diagnosed MJO. This is consistent with the equatorial wave dynamics in existing theories of MJO propagation. The MJO also affects the speed of gravity waves propagating offshore from the Maritime Continent islands. This is largely consistent with changes in static stability during the MJO cycle. The MJO and its interaction with the diurnal cycle are investigated in HiGEM, a high-resolution coupled model. Unlike many models, HiGEM represents the MJO well with eastward-propagating variability on intra-seasonal time scales at the correct zonal wavenumber, although the inter-tropical convergence zone's precipitation peaks strongly at the wrong time, interrupting the MJO's spatial structure. However, the modelled diurnal cycle is too weak and its phase is too early over land. The modulation of the diurnal amplitude by the MJO is also too weak and accounts for only 51% of the variability in MJO precipitation. Implications for forecasting and possible causes of the model errors are discussed, and further modelling studies are proposed.

What does “retrofit” mean, and how can we scale up action in the UK sector?

Purpose – Progress in retrofitting the UK's commercial properties continues to be slow and fragmented. New research from the UK and USA suggests that radical changes are needed to drive large-scale retrofitting, and that new and innovative models of financing can create new opportunities. The purpose of this paper is to offer insights into the terminology of retrofit and the changes in UK policy and practice that are needed to scale up activity in the sector. Design/methodology/approach – The paper reviews and synthesises key published research into commercial property retrofitting in the UK and USA and also draws on policy and practice from the EU and Australia. Findings – The paper provides a definition of “retrofit”, and compares and contrasts this with “refurbishment” and “renovation” in an international context. The paper summarises key findings from recent research and suggests that there are a number of policy and practice measures which need to be implemented in the UK for commercial retrofitting to succeed at scale. These include improved funding vehicles for retrofit; better transparency in actual energy performance; and consistency in measurement, verification and assessment standards. Practical implications – Policy and practice in the UK needs to change if large-scale commercial property retrofit is to be rolled out successfully. This requires mandatory legislation underpinned by incentives and penalties for non-compliance. Originality/value – This paper synthesises recent research to provide a set of policy and practice recommendations which draw on international experience, and can assist on implementation in the UK.

Variational data assimilation for very large environmental problems

Variational data assimilation is commonly used in environmental forecasting to estimate the current state of the system from a model forecast and observational data. The assimilation problem can be written simply in the form of a nonlinear least squares optimization problem. However the practical solution of the problem in large systems requires many careful choices to be made in the implementation. In this article we present the theory of variational data assimilation and then discuss in detail how it is implemented in practice. Current solutions and open questions are discussed.

Efficient incorporation of channel cross-section geometry uncertainty into regional and global scale flood inundation models

This paper investigates the challenge of representing structural differences in river channel cross-section geometry for regional to global scale river hydraulic models and the effect this can have on simulations of wave dynamics. Classically, channel geometry is defined using data, yet at larger scales the necessary information and model structures do not exist to take this approach. We therefore propose a fundamentally different approach where the structural uncertainty in channel geometry is represented using a simple parameterization, which could then be estimated through calibration or data assimilation. This paper first outlines the development of a computationally efficient numerical scheme to represent generalised channel shapes using a single parameter, which is then validated using a simple straight channel test case and shown to predict wetted perimeter to within 2% for the channels tested. An application to the River Severn, UK is also presented, along with an analysis of model sensitivity to channel shape, depth and friction. The channel shape parameter was shown to improve model simulations of river level, particularly for more physically plausible channel roughness and depth parameter ranges. Calibrating channel Manning’s coefficient in a rectangular channel provided similar water level simulation accuracy in terms of Nash-Sutcliffe efficiency to a model where friction and shape or depth were calibrated. However, the calibrated Manning coefficient in the rectangular channel model was ~2/3 greater than the likely physically realistic value for this reach and this erroneously slowed wave propagation times through the reach by several hours. Therefore, for large scale models applied in data sparse areas, calibrating channel depth and/or shape may be preferable to assuming a rectangular geometry and calibrating friction alone.

Decadal predictability of regional-scale peak winds over Europe using the Earth System Model of the Max-Planck-Institute for Meteorology

The predictability of high impact weather events on multiple time scales is a crucial issue both in scientific and socio-economic terms. In this study, a statistical-dynamical downscaling (SDD) approach is applied to an ensemble of decadal hindcasts obtained with the Max-Planck-Institute Earth System Model (MPI-ESM) to estimate the decadal predictability of peak wind speeds (as a proxy for gusts) over Europe. Yearly initialized decadal ensemble simulations with ten members are investigated for the period 1979–2005. The SDD approach is trained with COSMO-CLM regional climate model simulations and ERA-Interim reanalysis data and applied to the MPI-ESM hindcasts. The simulations for the period 1990–1993, which was characterized by several windstorm clusters, are analyzed in detail. The anomalies of the 95 % peak wind quantile of the MPI-ESM hindcasts are in line with the positive anomalies in reanalysis data for this period. To evaluate both the skill of the decadal predictability system and the added value of the downscaling approach, quantile verification skill scores are calculated for both the MPI-ESM large-scale wind speeds and the SDD simulated regional peak winds. Skill scores are predominantly positive for the decadal predictability system, with the highest values for short lead times and for (peak) wind speeds equal or above the 75 % quantile. This provides evidence that the analyzed hindcasts and the downscaling technique are suitable for estimating wind and peak wind speeds over Central Europe on decadal time scales. The skill scores for SDD simulated peak winds are slightly lower than those for large-scale wind speeds. This behavior can be largely attributed to the fact that peak winds are a proxy for gusts, and thus have a higher variability than wind speeds. The introduced cost-efficient downscaling technique has the advantage of estimating not only wind speeds but also estimates peak winds (a proxy for gusts) and can be easily applied to large ensemble datasets like operational decadal prediction systems.

CDASH: a cloud-enabled program for structure solution from powder diffraction data

The simulated annealing approach to crystal structure determination from powder diffraction data, as implemented in the DASH program, is readily amenable to parallelization at the individual run level. Very large scale increases in speed of execution can be achieved by distributing individual DASH runs over a network of computers. The CDASH program delivers this by using scalable on-demand computing clusters built on the Amazon Elastic Compute Cloud service. By way of example, a 360 vCPU cluster returned the crystal structure of racemic ornidazole (Z0 = 3, 30 degrees of freedom) ca 40 times faster than a typical modern quad-core desktop CPU. Whilst used here specifically for DASH, this approach is of general applicability to other packages that are amenable to coarse-grained parallelism strategies.

Scale interactions between the MJO and the western maritime continent

State-of-the-art regional climate model simulations that are able to resolve key mesoscale circulations are used, for the first time, to understand the interaction between the large-scale convective environment of the MJO and processes governing the strong diurnal cycle over the islands of the Maritime Continent (MC). Convection is sustained in the late afternoon just inland of the coasts due to sea breeze convergence. Previous work has shown that the variability in MC rainfall associated with the MJO is manifested in changes to this diurnal cycle; land-based rainfall peaks before the active convective envelope of the MJO reaches the MC, whereas oceanic rainfall rates peak whilst the active envelope resides over the region. The model simulations show that the main controls on oceanic MC rainfall in the early active MJO phases are the large-scale environment and atmospheric stability, followed by high oceanic latent heat flux forced by high near-surface winds in the later active MJO phases. Over land, rainfall peaks before the main convective envelope arrives (in agreement with observations), even though the large-scale convective environment is only moderately favourable for convection. The causes of this early rainfall peak are convective triggers from land-sea breeze circulations that are strong due to high surface insolation and surface heating. During the peak MJO phases cloud cover increases and surface insolation decreases, which weakens the strength of the mesoscale circulations and reduces land-based rainfall, even though the large-scale environment remains favourable for convection at this time. Hence, scale interactions are an essential part of the MJO transition across the MC.

Continental and global scale flood forecasting systems

Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not exist. As numerical weather prediction models continue to improve, operational centres are increasingly using the meteorological output from these to drive hydrological models, creating hydrometeorological systems capable of forecasting river flow and flood events at much longer lead times than has previously been possible. Furthermore, developments in, for example, modelling capabilities, data and resources in recent years have made it possible to produce global scale flood forecasting systems. In this paper, the current state of operational large scale flood forecasting is discussed, including probabilistic forecasting of floods using ensemble prediction systems. Six state-of-the-art operational large scale flood forecasting systems are reviewed, describing similarities and differences in their approaches to forecasting floods at the global and continental scale. Currently, operational systems have the capability to produce coarse-scale discharge forecasts in the medium-range and disseminate forecasts and, in some cases, early warning products, in real time across the globe, in support of national forecasting capabilities. With improvements in seasonal weather forecasting, future advances may include more seamless hydrological forecasting at the global scale, alongside a move towards multi-model forecasts and grand ensemble techniques, responding to the requirement of developing multi-hazard early warning systems for disaster risk reduction.

Can site and landscape-scale environmental attributes buffer bird populations against weather events?

Projected impacts of climate change on the populations and distributions of species pose a challenge for conservationists. In response, a number of adaptation strategies to enable species to persist in a changing climate have been proposed. Management to maximise the quality of habitat at existing sites may reduce the magnitude or frequency of climate-driven population declines. In addition large-scale management of landscapes could potentially improve the resilience of populations by facilitating inter-population movements. A reduction in the obstacles to species’ range expansion, may also allow species to track changing conditions better through shifts to new locations, either regionally or locally. However, despite a strong theoretical base, there is limited empirical evidence to support these management interventions. This makes it difficult for conservationists to decide on the most appropriate strategy for different circumstances. Here extensive data from long-term monitoring of woodland birds at individual sites are used to examine the two-way interactions between habitat and both weather and population count in the previous year. This tests the extent to which site-scale and landscape-scale habitat attributes may buffer populations against variation in winter weather (a key driver of woodland bird population size) and facilitate subsequent population growth. Our results provide some support for the prediction that landscape-scale attributes (patch isolation and area of woodland habitat) may influence the ability of some woodland bird species to withstand weather-mediated population declines. These effects were most apparent among generalist woodland species. There was also evidence that several, primarily specialist, woodland species are more likely to increase following population decline where there is more woodland at both site and landscape scales. These results provide empirical support for the concept that landscape-scale conservation efforts may make the populations of some woodland bird species more resilient to climate change. However in isolation, management is unlikely to provide a universal benefit to all species.

Decadal predictability of regional scale wind speed and wind energy potentials over Central Europe

Decadal predictions on timescales from one year to one decade are gaining importance since this time frame falls within the planning horizon of politics, economy and society. The present study examines the decadal predictability of regional wind speed and wind energy potentials in three generations of the MiKlip (‘Mittelfristige Klimaprognosen’) decadal prediction system. The system is based on the global Max-Planck-Institute Earth System Model (MPI-ESM), and the three generations differ primarily in the ocean initialisation. Ensembles of uninitialised historical and yearly initialised hindcast experiments are used to assess the forecast skill for 10 m wind speeds and wind energy output (Eout) over Central Europe with lead times from one year to one decade. With this aim, a statistical-dynamical downscaling (SDD) approach is used for the regionalisation. Its added value is evaluated by comparison of skill scores for MPI-ESM large-scale wind speeds and SDD-simulated regional wind speeds. All three MPI-ESM ensemble generations show some forecast skill for annual mean wind speed and Eout over Central Europe on yearly and multi-yearly time scales. This forecast skill is mostly limited to the first years after initialisation. Differences between the three ensemble generations are generally small. The regionalisation preserves and sometimes increases the forecast skills of the global runs but results depend on lead time and ensemble generation. Moreover, regionalisation often improves the ensemble spread. Seasonal Eout skills are generally lower than for annual means. Skill scores are lowest during summer and persist longest in autumn. A large-scale westerly weather type with strong pressure gradients over Central Europe is identified as potential source of the skill for wind energy potentials, showing a similar forecast skill and a high correlation with Eout anomalies. These results are promising towards the establishment of a decadal prediction system for wind energy applications over Central Europe.

A novel approach for distributed application scheduling based on prediction of communication events

The evolution of commodity computing lead to the possibility of efficient usage of interconnected machines to solve computationally-intensive tasks, which were previously solvable only by using expensive supercomputers. This, however, required new methods for process scheduling and distribution, considering the network latency, communication cost, heterogeneous environments and distributed computing constraints. An efficient distribution of processes over such environments requires an adequate scheduling strategy, as the cost of inefficient process allocation is unacceptably high. Therefore, a knowledge and prediction of application behavior is essential to perform effective scheduling. In this paper, we overview the evolution of scheduling approaches, focusing on distributed environments. We also evaluate the current approaches for process behavior extraction and prediction, aiming at selecting an adequate technique for online prediction of application execution. Based on this evaluation, we propose a novel model for application behavior prediction, considering chaotic properties of such behavior and the automatic detection of critical execution points. The proposed model is applied and evaluated for process scheduling in cluster and grid computing environments. The obtained results demonstrate that prediction of the process behavior is essential for efficient scheduling in large-scale and heterogeneous distributed environments, outperforming conventional scheduling policies by a factor of 10, and even more in some cases. Furthermore, the proposed approach proves to be efficient for online predictions due to its low computational cost and good precision. (C) 2009 Elsevier B.V. All rights reserved.

Scalable model-based configuration management of security services in complex enterprise networks

Security administrators face the challenge of designing, deploying and maintaining a variety of configuration files related to security systems, especially in large-scale networks. These files have heterogeneous syntaxes and follow differing semantic concepts. Nevertheless, they are interdependent due to security services having to cooperate and their configuration to be consistent with each other, so that global security policies are completely and correctly enforced. To tackle this problem, our approach supports a comfortable definition of an abstract high-level security policy and provides an automated derivation of the desired configuration files. It is an extension of policy-based management and policy hierarchies, combining model-based management (MBM) with system modularization. MBM employs an object-oriented model of the managed system to obtain the details needed for automated policy refinement. The modularization into abstract subsystems (ASs) segment the system-and the model-into units which more closely encapsulate related system components and provide focused abstract views. As a result, scalability is achieved and even comprehensive IT systems can be modelled in a unified manner. The associated tool MoBaSeC (Model-Based-Service-Configuration) supports interactive graphical modelling, automated model analysis and policy refinement with the derivation of configuration files. We describe the MBM and AS approaches, outline the tool functions and exemplify their applications and results obtained. Copyright (C) 2010 John Wiley & Sons, Ltd.

Patterns of water and heat flux across a biome gradient from tropical forest to savanna in Brazil

We investigated the seasonal patterns of water vapor and sensible heat flux along a tropical biome gradient from forest to savanna. We analyzed data from a network of flux towers in Brazil that were operated within the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA). These tower sites included tropical humid and semideciduous forest, transitional forest, floodplain (with physiognomies of cerrado), and cerrado sensu stricto. The mean annual sensible heat flux at all sites ranged from 20 to 38 Wm(-2), and was generally reduced in the wet season and increased in the late dry season, coincident with seasonal variations of net radiation and soil moisture. The sites were easily divisible into two functional groups based on the seasonality of evaporation: tropical forest and savanna. At sites with an annual precipitation above 1900 mm and a dry season length less than 4 months (Manaus, Santarem and Rondonia), evaporation rates increased in the dry season, coincident with increased radiation. Evaporation rates were as high as 4.0 mm d(-1) in these evergreen or semidecidous forests. In contrast, ecosystems with precipitation less than 1700 mm and a longer dry season (Mato Grosso, Tocantins and Sao Paulo) showed clear evidence of reduced evaporation in the dry season. Evaporation rates were as low as 2.5 mm d(-1) in the transitional forests and 1 mm d(-1) in the cerrado. The controls on evapotranspiration seasonality changed along the biome gradient, with evaporative demand (especially net radiation) playing a more important role in the wetter forests, and soil moisture playing a more important role in the drier savannah sites.