ALBERT

Description: This study has investigated serial (temporal) clustering of extra-tropical cyclones simulated by 17 climate models that participated in CMIP5. Clustering was estimated by calculating the dispersion (ratio of variance to mean) of 30 December-February counts of Atlantic storm tracks passing nearby each grid point. Results from single historical simulations of 1975-2005 were compared to those from historical ERA40 reanalyses from 1958-2001 ERA40 and single future model projections of 2069-2099 under the RCP4.5 climate change scenario. Models were generally able to capture the broad features in reanalyses reported previously: underdispersion/regularity (i.e. variance less than mean) in the western core of the Atlantic storm track surrounded by overdispersion/clustering (i.e. variance greater than mean) to the north and south and over western Europe. Regression of counts onto North Atlantic Oscillation (NAO) indices revealed that much of the overdispersion in the historical reanalyses and model simulations can be accounted for by NAO variability. Future changes in dispersion were generally found to be small and not consistent across models. The overdispersion statistic, for any 30 year sample, is prone to large amounts of sampling uncertainty that obscures the climate change signal. For example, the projected increase in dispersion for storm counts near London in the CNRMCM5 model is 0.1 compared to a standard deviation of 0.25. Projected changes in the mean and variance of NAO are insufficient to create changes in overdispersion that are discernible above natural sampling variations.

Description: The drivers of background tree mortality rates – the typical low rates of tree mortality found in forests in the absence of acute stresses like drought – are central to our understanding of forest dynamics, the effects of ongoing environmental changes on forests, and the causes and consequences of geographical gradients in the nature and strength of biotic interactions. To shed light on factors contributing to background tree mortality, we analyzed detailed pathological data from 200,668 tree-years of observation and 3729 individual tree deaths, recorded over a 13-year period in a network of permanent forest plots in California's Sierra Nevada mountain range. We found that: (1) Biotic mortality factors (mostly insects and pathogens) dominated (58%), particularly in larger trees (86%). Bark beetles were the most prevalent (40%), even though there were no outbreaks during the study period; in contrast, the contribution of defoliators was negligible. (2) Relative occurrences of broad classes of mortality factors (biotic, 58%; suppression, 51%; and mechanical, 25%) are similar among tree taxa, but may vary with tree size and growth rate. (3) We found little evidence of distinct groups of mortality factors that predictably occur together on trees. Our results have at least three sets of implications. First, rather than being driven by abiotic factors such as lightning or windstorms, the “ambient” or “random” background mortality that many forest models presume to be independent of tree growth rate is instead dominated by biotic agents of tree mortality, with potentially critical implications for forecasting future mortality. Mechanistic models of background mortality, even for healthy, rapidly-growing trees, must therefore include the insects and pathogens that kill trees. Second, the biotic agents of tree mortality, instead of occurring in a few predictable combinations, may generally act opportunistically and with a relatively large degree of independence from one another. Finally, beyond the current emphasis on folivory and leaf defenses, studies of broad-scale gradients in the nature and strength of biotic interactions should also include biotic attacks on, and defenses of, tree stems and roots. This article is protected by copyright. All rights reserved.

Description: ABSTRACT This study considers long-term precipitation and temperature variability across the Caribbean using two gridded data sets (CRU TS 3.21 and GPCCv5). We look at trends across four different regions (Northern, Eastern, Southern and Western), for three different seasons (May to July, August to October and November to April) and for three different periods (1901–2012, 1951–2012 and 1979–2012). There are no century-long trends in precipitation in either data set, although all regions (with the exception of the Northern Caribbean) show decade-long periods of wetter or drier conditions. The most significant of these is for the Southern Caribbean region which was wetter than the 1961–1990 average from 1940 to 1956 and then drier from 1957 to 1965. Temperature in contrast shows statistically significant warming everywhere for the periods 1901–2012, 1951–2012 and for over half the area during 1979–2012. Data availability is a limiting issue over much of the region and we also discuss the reliability of the series we use in the context of what is known to be available in the CRU TS 3.21 data set. More station data have been collected but have either not been fully digitized yet or not made freely available both within and beyond the region.

Description: Temperature and current measurements from two moorings onshore of the Celtic Sea shelf break, a well-known hot-spot for tidal energy conversion, show the impact of passing summer storms on the baroclinic wave field. Wind-driven vertical mixing changed stratification to permit an increased on-shelf energy transport, and baroclinic energy in the semidiurnal band appeared at the moorings 1–4 days after the storm mixed the upper 50 m of the water column. The timing of the maximum in the baroclinic energy flux is consistent with the propagation of the semidiurnal internal tide from generation sites at the shelf break to the moorings 40 km away. Also, the ~3 day duration of the peak in M 2 baroclinic energy flux at the moorings corresponds to the restratification time scale following the first storm.

Description: This study investigates whether or not predictability always decreases for more extreme events. Predictability is measured by the Mean Squared Error (MSE), estimated here from the difference of pairs of ensemble forecasts, conditioned on one of the forecast variables (the “pseudo-observation”) exceeding a threshold. Using an exchangeable linear regression model for pairs of forecast variables, we show that the MSE can be decomposed into the sum of three terms: a threshold-independent constant, a mean term that always increases with threshold, and a variance term that can either increase, decrease, or stay constant with threshold. Using the Generalised Pareto Distribution to model wind speed excesses over a threshold, we show that MSE always increases with threshold at sufficiently high threshold. However, MSE can be a decreasing function of threshold at lower thresholds but only if the forecasts have finite upper bounds. The methods are illustrated by application to daily wind speed forecasts for London made using the 24 member Met Office Global and Regional Ensemble Prediction System from 1 Jan 2009 to 31 May 2011. For this example, the mean term increases faster than the variance term decreases with increasing threshold, and so predictability decreases for more extreme events.

Description: We estimate the potential predictability of European winter temperature using factors based on physical studies of their influences on European winter climate. These influences include sea surface temperature patterns in different oceans, major tropical volcanoes, the quasi-biennial oscillation in the tropical stratosphere, and anthropogenic climate change. We first assess the predictive skill for winter mean temperature in northern Europe by evaluating statistical hindcasts made using multiple regression models of temperature for Europe for winter and the January–February season. We follow this up by extending the methodology to all of Europe on a 5° × 5° grid and include rainfall for completeness. These results can form the basis of practical prediction methods. However, our main aim is to develop ideas to act as a benchmark for improving the performance of dynamical climate models. Because we consider only potential predictability, many of the predictors have estimated values coincident with the winter season being forecast. However, in each case, these values are predictable on average with considerable skill in advance of the winter season. A key conclusion is that to reproduce the results of this paper, dynamical forecasting models will require a fully resolved stratosphere. Copyright © 2011 Royal Meteorological Society and British Crown copyright, the Met Office

Description: This article examines whether the temporal clustering of flood events can be explained in terms of climate variability or time-varying land-surface state variables. The point process modelling framework for flood occurrence is based on Cox processes, which can be represented as Poisson processes with randomly varying rate of occurrence. In the special case that the rate of occurrence is deterministic, the Cox process simplifies to a Poisson process. Poisson processes represent flood occurrences which are not clustered. The Cox regression model is used to examine the dependence of the rate of occurrence on covariate processes. We focus on 41 stream gauge stations in Iowa, with discharge records covering the period 1950–2009. The climate covariates used in this study are the North Atlantic Oscillation (NAO) and the Pacific/North American Teleconnection (PNA). To examine the influence of land-surface forcing on flood occurrence, the antecedent 30 d rainfall accumulation is considered. In 27 out of 41 stations, either PNA or NAO, or both are selected as significant predictors, suggesting that flood occurrence in Iowa is influenced by large-scale climate indices. Antecedent rainfall, used as a proxy for soil moisture, plays an important role in driving the occurrence of flooding in Iowa. These results point to clustering as an important element of the flood occurrence process. Copyright © 2012 Royal Meteorological Society

Description: A time series of monthly mean surface temperatures taken at Svalbard airport, Spitzbergen, for the period 1912–2010 was examined for changes in melt-season length. The annual melt-season length was constructed from daily temperature estimates based on the monthly data using smoothing splines. We argue that the changes in annual melt-season length are linked to variability in regional sea surface temperatures, the mean Northern Hemisphere surface temperature and the North Atlantic Oscillation (NAO) index. A regression model for the melt-season length with these three parameters as predictors, explained about 40% of the observed variance. The annual mean melt season for the period from 1912 to 2010 was estimated to be 108 days, and the linear trend was 0.17 days/year. The risk of having positive extremes in the melt season increased with increasing Northern Hemisphere surface temperature and the regional sea surface temperatures. On the basis of our study of past observations, the 100-year return length of the melt season at Svalbard was predicted to change from the current 95% confidence interval of 131 (108, 138) days to 175 (109, 242) days with 1 °C warming of both regional sea surface temperature and the mean Northern Hemisphere surface temperature. Copyright © 2011 Royal Meteorological Society

Description: Under global warming the Caribbean is projected to be significantly drier by century's end during its primary rainy season from May to November. The PRECIS regional model is used to simulate the end-of-century (2071–2100) manifestation of the Caribbean Low Level Jet (CLLJ) under two Intergovernmental Panel on Climate Change (IPCC) global warming scenarios. The CLLJ is a feature of the Intra-American seas which during its July peak is dynamically linked to a brief mid-summer drying and interruption of the Caribbean rainy season. The regional model captures the CLLJ's present-day spatial and temporal characteristics reasonably well, simulating both the boreal winter (February) and summer (July) peaks. Under global warming there is an intensification of the CLLJ's core strength from May through November. The intensification is such that by October the CLLJ is of comparable core strength to its present-day peak in July. The persistence of the strong CLLJ beyond July and through November is linked to the perpetuation of a dry pattern in the Caribbean in the future. In contrast, the boreal winter manifestation of the CLLJ is largely unaltered in the future. Copyright © 2012 Royal Meteorological Society