Relating remotely sensed forest damage data to wind data: storms Lothar (1999) and Vivian (1990) in Switzerland

This study compares the surface wind speed and forest damage data of two exceptionally severe winter storms, Vivian 1990 and Lothar 1999. The study area comprises the region that suffered damage in Switzerland. The wind speed data were derived from simulations of MeteoSwiss (Federal Office of Meteorology and Climatology), measurements during the storm periods and expert analyses of the data. The remotely sensed forest damage data were provided by the Federal Office for the Environment and the forest cover data by Swiss Federal Statistical Office. We compared data on the peak gust and maximum average wind speed, with data on the spatially related forest area and forest damage area, and found some clear differences in the correlations between the different wind data and forest damage. Our results point generally to the damage-causing role of near-surface gusts at maximum wind speeds during the storm. These tended to be spatially distributed on a fine scale. In only a few cases were the results statistically significant. However, these results could probably be improved with better wind data. For example, gust measurements spatially closer to forests or simulations of gusts at maximum wind speed could be produced with a spatially higher resolution.     

Variations in precipitation amounts, atmosphere circulation, and relative humidity in high mountainous parts of Bulgaria for the period 1947–2008

Variations of precipitation amounts, atmosphere circulation, and relative humidity at three high mountainous stations in Bulgaria are investigated. The research period is 1947–2008. Three stations are peaks Musala (2,927?m), Cherni Vrah (2,293?m), and Botev (2,378?m). Homogeneity checks were performed. Some adjustments of monthly precipitation amounts were done due to the change of rain gauge placements. Methods employed in this research are statistical—trend analysis, multiple linear regression, autocorrelation, spectral analysis, correlation, etc. The main conclusion is that annual precipitation amounts in high mountainous parts of Bulgaria decrease over the last six decades. These trends are statistically significant in lower and northern stations of Cherni Vrah and Botev. Observed tendencies are due mainly to precipitation in months of the cold part of the year—from October till March and also June. Atmospheric circulation, on an annual basis, shows a decrease in the number of cyclones and an increase of the number of anticyclones. Atmospheric pressure has no significant change during the research period. Relative humidity, on an annual basis, increases at the two western stations (Musala and Cherni Vrah) and decreases at Botev peak. Multiple regression statistical modeling shows good results at stations Cherni Vrah and Botev. Station Musala is more difficult to model with the five predictors used because of the winter months. Correlation coefficients reveal a good connection between precipitation amounts at all three stations. Only Botev station has a significant cycle in precipitation distribution, which is about 3.7?months.     

Evolution and modulation of tropical heating from the last glacial maximum through the twenty-first century

Twentieth century observations show that during the last 50?years the sea-surface temperature (SST) of the tropical oceans has increased by ~0.5°C and the area of SST >26.5 and 28°C (arbitrarily referred to as the oceanic warm pool: OWP) by 15 and 50% respectively in association with an increase in green house gas concentrations, with non-understood natural variability or a combination of both. Based on CMIP3 projections the OWP is projected to double during twenty-first century in a moderate CO₂ forcing scenario (IPCC A1B scenario). However, during the observational period the area of positive atmospheric heating (referred to as the dynamic warm pool, DWP), has remained constant. The threshold SST (T _H ), which demarks the region of net heating and cooling, has increased from 26.6°C in the 1950s to 27.1°C in the last decade and it is projected to increase to ~28.5°C by 2100. Based on climate model simulations, the area of the DWP is projected to remain constant during the twenty-first century. Analysis of the paleoclimate model intercomparison project (PMIP I and II) simulations for the Last Glacial maximum and the Mid-Holocene periods show a very similar behaviour, with a larger OWP in periods of elevated tropical SST, and an almost constant DWP associated with a varying T _H . The constancy of the DWP area, despite shifts in the background SST, is shown to be the result of a near exact matching between increases in the integrated convective heating within the DWP and the integrated radiative cooling outside the DWP as SST changes. Although the area of the DWP remains constant, the total tropical atmospheric heating is a strong function of the SST. For example the net heating has increased by about 10% from 1950 to 2000 and it is projected to increase by a further 20% by 2100. Such changes must be compensated by a more vigorous atmospheric circulation, with growth in convective heating within the warm pool, and an increase of subsiding air and stability outside the convective warm pool and an increase of vertical shear at the DWP boundaries. This finding is contrary to some conclusions from other studies but in accord with others. We discuss the similarities and differences at length.     

Seasonal prediction skill of ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter

The seasonal prediction skill for the Northern Hemisphere winter is assessed using retrospective predictions (1982–2010) from the ECMWF System 4 (Sys4) and National Center for Environmental Prediction (NCEP) CFS version 2 (CFSv2) coupled atmosphere–ocean seasonal climate prediction systems. Sys4 shows a cold bias in the equatorial Pacific but a warm bias is found in the North Pacific and part of the North Atlantic. The CFSv2 has strong warm bias from the cold tongue region of the eastern Pacific to the equatorial central Pacific and cold bias in broad areas over the North Pacific and the North Atlantic. A cold bias in the Southern Hemisphere is common in both reforecasts. In addition, excessive precipitation is found in the equatorial Pacific, the equatorial Indian Ocean and the western Pacific in Sys4, and in the South Pacific, the southern Indian Ocean and the western Pacific in CFSv2. A dry bias is found for both modeling systems over South America and northern Australia. The mean prediction skill of 2 meter temperature (2mT) and precipitation anomalies are greater over the tropics than the extra-tropics and also greater over ocean than land. The prediction skill of tropical 2mT and precipitation is greater in strong El Nino Southern Oscillation (ENSO) winters than in weak ENSO winters. Both models predict the year-to-year ENSO variation quite accurately, although sea surface temperature trend bias in CFSv2 over the tropical Pacific results in lower prediction skill for the CFSv2 relative to the Sys4. Both models capture the main ENSO teleconnection pattern of strong anomalies over the tropics, the North Pacific and the North America. However, both models have difficulty in forecasting the year-to-year winter temperature variability over the US and northern Europe.     

A comparison of shelf observation platforms for assimilation in an eddy-resolving ocean model

An assessment of the likely benefits of assimilating in situ temperature (T) and salinity (S) observations from repeat glider transects and surface velocity observations from high-frequency radar arrays into an eddy-resolving ocean model is presented. The deployment of new shelf observation platforms around Australia is being undertaken through the Australian Integrated Marine Observing System program. In this study, various options for an observing system along the coast of New South Wales, Australia, are assessed for their benefits to an ocean forecast and reanalysis system. The forecast system considered here uses ensemble optimal interpolation (EnOI) for data assimilation. Using error estimates from the EnOI scheme, estimates of the theoretical analysis errors are calculated for different observing systems that include a range of remotely sensed and in situ observations. The results demonstrate that if HF radar observations are assimilated along with the standard components of the global ocean observing system, the analysis errors are likely to reduce by as much as 80% for velocity and 60% for T, S and sea-level in the vicinity of the observations. Owing to the relatively short along-shore decorrelation length-scales for T and S near the shelf, the glider observations are likely to provide the forecast system with a more modest gain.     

Statistical Downscaling of Wind Variability from Meteorological Fields

Measurements show that on numerous occasions the low-level wind is highly variable across a large portion of south-eastern Australia. Under such conditions the risk of a large rapid change in total wind power is increased. While variability tends to increase with mean wind speed, a large component of wind variability is not explained by wind speed alone. In this work, reanalysis fields from the US National Centers for Environmental Prediction (NCEP) are statistically downscaled to model wind variability at a coastal location in Victoria, Australia. In order to reduce the dimensionality of the problem, the NCEP fields are each decomposed using empirical orthogonal function (EOF) techniques. The downscaling technique is applied to two periods in the seasonal cycle, namely (i) winter to early spring, and (ii) summer. In each case, data representing 2 years are used to form a model that is then validated using independent data from another year. The EOFs that best predict wind variability are examined. To allow for non-linearity and complex interaction between variables, all empirical models are built using random forests. Quantitatively, the model compares favourably with a simple regression of wind variability against wind speed, as well as multiple linear regression models.     

Ocean–atmosphere coupling and the boreal winter MJO

The influence of ocean–atmosphere coupling on the simulation and prediction of the boreal winter Madden–Julian Oscillation (MJO) is examined using the Seoul National University coupled general circulation model (CGCM) and atmospheric—only model (AGCM). The AGCM is forced with daily SSTs interpolated from pentad mean CGCM SSTs. Forecast skill is examined using serial extended simulations spanning 26 different winter seasons with 30-day forecasts commencing every 5 days providing a total of 598 30-day simulations. By comparing both sets of experiments, which share the same atmospheric components, the influence of coupled ocean–atmosphere processes on the simulation and prediction of MJO can be studied. The mean MJO intensity possesses more realistic amplitude in the CGCM than in AGCM. In general, the ocean–atmosphere coupling acts to improve the simulation of the spatio-temporal evolution of the eastward propagating MJO and the phase relationship between convection (OLR) and SST over the equatorial Indian Ocean and the western Pacific. Both the CGCM and observations exhibit a near-quadrature relationship between OLR and SST, with the former lagging by about two pentads. However, the AGCM shows a less realistic phase relationship. As the initial conditions are the same in both models, the additional forcing by SST anomalies in the CGCM extends the prediction skill beyond that of the AGCM. To test the applicability of the CGCM to real-time prediction, we compute the Real-time Multivariate MJO (RMM) index and compared it with the index computed from observations. RMM1 (RMM2) falls away rapidly to 0.5 after 17–18 (15–16) days in the AGCM and 18–19 (16–17) days in the CGCM. The prediction skill is phase dependent in both the CGCM and AGCM.     

Participatory modeling in dairy farm systems: a method for building consensual environmental sustainability using seasonal climate forecasts

Dairy farmers face increasing pressure to decrease environmental impact while remaining economically viable. Adaptation of farm management practices in response to seasonal climate forecasts may be one means of achieving these objectives. This paper describes the interactive and iterative process by which farmers, researchers, extension agents, regulatory agencies, and other stakeholders collaborated to create, calibrate, and validate the Dynamic North Florida Dairy Farm model (DyNoFlo), a whole-farm decision support system to decrease nitrogen leaching while maintaining profitability under variable climate conditions. Participatory modeling may enhance the creation of adoptable and adaptable user-friendly models that include environmental, economic and biophysical components. By providing farmers, policy makers, and other stakeholders with a more holistic view of current practices, common ground among them was more easily identified and collaboration was fostered. Farmer values included willingness to be good environmental stewards when they are profitable. The participatory research and development process enhanced understanding of and potential adaptation to seasonal climate variability conditioned to the El Niño Southern Oscillation (ENSO) phases in light of increasing environmental regulations and economic challenges. Adoption of the collaboratively-developed DyNoFlo is expected to be higher than usual because stakeholders feel greater ownership of the final product.     

A robust sequential CO2 emissions strategy based on optimal control of atmospheric CO2 concentrations

This paper formally introduces the concept of mitigation as a stochastic control problem. This is illustrated by applying a digital state variable feedback control approach known as Non-Minimum State Space (NMSS) control to the problem of specifying carbon emissions to control atmospheric CO₂ concentrations in the presence of uncertainty. It is shown that the control approach naturally lends itself to integrating both anticipatory and reflexive mitigation strategies within a single unified framework. The framework explicitly considers the closed-loop nature of climate mitigation, and employs a policy orientated optimisation procedure to specify the properties of this closed-loop system. The product of this exercise is a control law that is suitably conditioned to regulate atmospheric CO₂ concentrations through assimilating online information within a 25-year review cycle framework. It is shown that the optimal control law is also robust when faced with significant levels of uncertainty about the functioning of the global carbon cycle.