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: 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: Climate models exhibit large biases in sea ice area (SIA) in their historical simulations. This study has explored the impacts of these biases on multi-model uncertainty in CMIP5 ensemble projections of 21 st century change in Antarctic surface temperature, net precipitation and SIA. The analysis is based on time slice climatologies in the RCP8.5 future scenario (2070–2099) and historical (1970–1999) simulations across 37 different CMIP5 models. Projected changes in net precipitation, temperature and SIA are found to be strongly associated with simulated historical mean SIA (e.g. cross-model correlations of r = 0.77, 0.71 and −0.85, respectively). Furthermore, historical SIA bias is found to have a large impact on the simulated ratio between net precipitation response and temperature response. This ratio is smaller in models with smaller-than-observed SIA. These strong emergent relationships on SIA bias could, if found to be physically robust, be exploited to give more precise climate projections for Antarctica.

Description: Abstract Deep sedimentary basins amplify long‐period shaking from seismic waves, increasing the seismic hazard for cities sited on such basins. We perform 3‐D simulations of point source earthquakes distributed around the Seattle and Tacoma basins in Washington State to examine the dependence of basin amplification on source azimuth, depth, and earthquake type. For periods between 1 and 10 s, the pattern of amplification is spatially heterogeneous and differs considerably with the source‐to‐site azimuth. For close‐in earthquakes, the greatest basin amplification occurs toward the farside of the basin and ground motions from crustal earthquakes experience greater amplification than those from more vertically incident, deeper intraplate earthquakes. Love and Rayleigh waves form similar spatial patterns for a given source location, although the magnitude of amplification varies. The source dependence of basin amplification is an important factor for seismic hazard assessment, in both the Seattle and Tacoma basins, and by extension for deep sedimentary basins worldwide.

Description: Abstract Empirical equations for wave breaking and wave setup are compared with archived shoreline wave setup measurements to investigate the contribution of wind‐waves to extreme Mean Total Water Levels (MTWL, the mean height of the shoreline), for natural beaches exposed to open ocean wind‐waves. A broad range of formulations are compared through linear regression and quantile regression analysis of the highest measured values. Shoreline wave setup equations are selected based on the availability of local beach slope data and the ability of the quantile regression to show a good representation of the highest measured levels. Wave parameters from an existing spectral wave hindcast are used as input to the selected equations and are combined with a storm‐tide time series to quantify the relative contribution of shoreline wave setup to the extreme MTWL climate along Australian beaches. A multi‐pass analysis is provided to understand the ability to capture the shoreline wave setup estimates with and without considering beach slope. The national scale analysis which does not include beach slope indicates there are multiple contributing factors to MTWL. Examples are provided at two locations of differing local beach slope to show the importance of including local beach slope in determining the contribution of waves to MTWL. A tool is in development for further investigation of wave setup for Australian beaches.

Description: Ensemble forecasts are routinely used as a basis for probabilistic predictions. The skill of probabilistic predictions derived from ensemble forecasts depends on the number of ensemble members. We derive a new verification score, called the ensemble‐adjusted Ignorance Score, which can correct for the effect of limited ensemble size and therefore allows for a more robust comparison of forecasts based on different ensemble sizes. The unadjusted Ignorance Score (solid line) depends on the ensemble size m, assigning higher (worse) scores to smaller ensembles drawn from the same forecast distribution. The ensemble‐adjusted Ignorance Score (dashed line) proposed here does not depend on ensemble size and thus allows for a fair comparison of equivalent ensembles of different sizes. This study considers the application of the Ignorance Score (IS, also known as the Logarithmic Score) for ensemble verification. In particular, we consider the case where an ensemble forecast is transformed to a normal forecast distribution, and this distribution is evaluated by the IS. It is shown that the IS systematically depends on the ensemble size, such that larger ensembles yield better expected scores. An ensemble‐adjusted IS is proposed, which extrapolates the score of an m‐member ensemble to the score that the ensemble would achieve if it had fewer or more than m members. Using the ensemble adjustment, a fair version of the IS is derived, which is optimized if ensembles are statistically consistent with the observations. The benefit of the ensemble adjustment is illustrated by comparing ISs of ensembles of different sizes in a seasonal climate forecasting context and a medium‐range weather forecasting context. An ensemble‐adjusted score can be used for a fair comparison between ensembles of different sizes, and to accurately estimate the expected score of a large operational ensemble by running a much smaller hindcast ensemble.

Description: This study assesses the skill of four statistical models in hindcasting North Atlantic annual tropical cyclone (TC) frequency over 1950–2008 with the aim of projecting future activity. Three of the models are motivated by operational statistical forecast schemes and are premised on standard hurricane predictors including sea surface temperatures (SSTs) and near-surface zonal winds. The fourth model uses an SST-gradient index previously proposed for Caribbean seasonal rainfall prediction. The statistical models, created from backward regression, explain 24-48% of the observed variability in 1950-2008 annual TC frequency. The future state of the predictors are extracted from the ECHAM5, HadCM3, MRI CGCM2.3.2a and MIROC3.2 GCM simulations under the Coupled Model Intercomparison Project Phase 3 (CMIP3). Models utilizing SST and near-surface wind predictors suggest significant increases in mean annual frequency by 2-8 TCs by 2070-2090, compared to a single surface wind predictor model, indicating that positive trends in SSTs under global warming have a larger relative influence on projections than changes in the variability of the surface winds. Wind-only models exhibit declines in TC frequency while the SST-gradient model yields little change relative to the present-day mean. Backward regression reapplied against the 1990-2008 period, analogous to future warmer oceanic and atmospheric state relative to the earlier years in the record, retains only the CLLJ-type predictors, explaining up to 82% of TC frequency variability and suggesting a more dominant role for the CLLJ in a warmer climate. Projections using the new models show either a more conservative increase or a stronger decrease in frequency, consistent with a stronger CLLJ.

Description: ABSTRACT The Statistical Downscaling Model (SDSM) is used to investigate future projections of daily minimum and maximum temperature extremes for 45 stations and rainfall extremes for 39 stations across the Caribbean and neighbouring regions. Models show good skill in reproducing the monthly climatology of the mean daily temperatures and the frequencies of warm days, warm nights, cool days and cool nights between 1961 and 2001. Models for rainfall exhibit lower skill but generally capture the monthly climatology of mean daily rainfall and the spatial distribution of the mean annual maximum number of consecutive dry days (CDD) and mean annual count of days with daily rainfall above 10 mm (R10). Future projections suggest an increase (decrease) in warm (cool) days and nights by 2071–2099 under the A2 and B2 scenarios relative to 1961–1990. An increase in CDD is suggested for most stations except some eastern Caribbean stations and Bahamas. Decreases in RX1 (monthly maximum 1-day precipitation), R10 and R95p (annual total rainfall above the 95th percentile) are also suggested for some northern Caribbean locations and Belize under the A2 scenario, compared to a mixture of increases and decreases for the eastern Caribbean. Atmospheric predictors used in SDSM correlate well with known oceanic and atmospheric drivers of Caribbean climate, e.g. the Atlantic Multidecadal Oscillation (AMO) on a seasonal timescale. Atlantic sea surface temperatures and the Caribbean low level jet appear to have significant influence on Caribbean temperature and rainfall extremes. Initiatives have explored future Caribbean rainfall and temperature extremes. These relied on outputs from global and regional climate models and a weather generator. Here, the Statistical Downscaling Model (SDSM), a hybrid of regression and stochastic weather generator approaches is used. Results suggest more frequent warm events and variable responses in extreme rainfall by 2071–2099 under A2 and B2 scenarios. Atmospheric predictors in SDSM correlate well with known oceanic and atmospheric drivers of Caribbean climate, e.g. Atlantic Multidecadal Oscillation (AMO) on seasonal timescales.