UK Meteorological Office Research Articles

Diurnal Variation in Urban Heat Island Intensity in Birmingham: The Relationship between Nocturnal Surface and Canopy Heat Islands

Urban heat islands have garnered significant attention due to their potential impact on human life. Previous studies on urban heat islands have focused on characterizing temporal and spatial variations over longer periods of time. In this study, we investigated the urban heat island (UHI) in Birmingham from September 2013 to August 2014 using higher temporal resolution SEVIRI satellite surface temperature data along with data from the Birmingham Urban Climate Laboratory (BUCL) meteorological station and the UK Meteorological Office meteorological station. Our aim was to characterize the diurnal variations in the surface urban heat island intensity (SUHII) and canopy urban heat island intensity (CUHII) and to explore their relationship under the influence of three factors (day/nighttime, season, and wind speed) using regression analysis. Our findings reveal that SUHII and CUHII exhibit relatively stable patterns at night but vary significantly during the day with opposite diurnal trends. In addition, SUHII and CUHII were more variable in spring and summer but less variable in winter. During the nighttime, SUHII represents CUHII with high confidence, especially during spring and summer, but less so during the cold season. In addition, SUHII represents CUHII with greater confidence under low-wind conditions. This study deepens our understanding of the diurnal dynamics of urban heat islands and the influence of atmospheric conditions on the relationship between surface and canopy heat islands in urban areas. The results of this study can be used for heat island studies in cities that lack high-precision observation networks and to guide sustainable urban development.

Was the extreme rainfall that caused the August 2022 flood in Pakistan predictable?

Pakistan suffered from severe floods in the past, but in August 2022, the country experienced exceptional extreme rainfall events that caused widespread and catastrophic flooding. The 2022 flood affected all aspects of socio-economic lives including agriculture, infrastructure, and mortality of humans and livestock. The two-day accumulated extreme rainfall on 17–18 August was anomalous and contributed the most to the flood in the southern provinces of Pakistan. The damage caused by extreme rainfall and the subsequent flooding has raised questions regarding the predictability of extreme rainfall by the existing weather forecasting models. Here, we use ensemble forecasts from four numerical weather prediction models under THORPEX Interactive Grand Global Ensemble datasets to examine the predictability of extreme rainfall at a six-day lead. The extreme precipitation during 17–18 August 2022 was predictable a week before the event that contributed the most to the flooding. All the forecast models provided an early warning at a six-day lead time. UK Meteorological Office and European Centre for Medium-Range Weather Forecasts models produced comparable results to observations at all the lead times. Our findings highlight that an integrated framework of extended quantitative precipitation forecasts and hydrological modeling can help reduce the country’s flood vulnerability and risk associated with it.

Heliospheric 3-D MHD ENLIL simulations of multi-CME and multi-spacecraft events

Interpreting multi-spacecraft heliospheric observations of the evolving solar wind (SW) streams with propagating and interacting coronal mass ejections (CMEs) is challenging. Numerical simulations can provide global context and suggest what may and may not be observed. The heliospheric three-dimensional (3D) magnetohydrodynamic (MHD) ENLIL model can provide a near-real-time prediction of heliospheric space weather, and it is used at NASA Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center (SWPC), and UK Meteorological Office (MetOffice). However, this version does not show its full potential, especially in the case of multi-CME events observed by various spacecraft. We describe tools developed to interpret remote observations and in-situ measurements better and apply them to multi-CME events observed by ACE, STEREO-A, Parker Solar Probe (PSP), BepiColombo, and Solar Orbiter. We present some results on 1) global structures of the SW speed and density at the ecliptic, 2) the evolution of SW parameters at the spacecraft, 3) magnetic field connectivity at the spacecraft, 4) automatic detection of shock parameters and alert plots, and 5) synthetic white-light (WL) imaging. This paper is not on model initialization or analyzing specific CME events, but it describes features not used at space weather prediction centers and provided by NASA/CCMC Run-On-Request service. This paper advertises new tools and shows their benefits when applied to selected heliospheric space weather events observed at near-Earth, PSP, Solar Orbiter, and STEREO-A spacecraft.

On the Relationship Between Reliability Diagrams and the “Signal‐To‐Noise Paradox”

AbstractThe “signal‐to‐noise paradox” for seasonal forecasts of the winter North Atlantic Oscillation (NAO) is often described as an “underconfident” forecast and measured using the ratio‐of‐predictable components (RPCs) metric. However, comparison of RPC with other measures of forecast confidence, such as spread‐error ratios, can give conflicting impressions, challenging this informal description. We show, using a linear statistical model, that the “paradox” is equivalent to a situation where the reliability diagram of any percentile forecast has a slope exceeding 1. The relationship with spread‐error ratios is shown to be far less direct. We furthermore compute reliability diagrams of winter NAO forecasts using seasonal hindcasts from the European Centre for Medium‐range Weather Forecasts and the UK Meteorological Office. While these broadly exhibit slopes exceeding 1, there is evidence of asymmetry between upper and lower terciles, indicating a potential violation of linearity/Gaussianity. The limitations and benefits of reliability diagrams as a diagnostic tool are discussed.

Decadal variation of the relationship between the previous autumn IOD and the South China Sea summer monsoon

Using reanalysis data from the ECMWF and Hadley Centre of the UK Meteorological Office, the present study analyzes the relationship between Indian Ocean Dipole (IOD) in the previous autumn (September–November) and South China Sea Summer (June–August) Monsoon (SCSSM) from 1951 to 2021. It is found that the relationship between the two indices was significantly enhanced from the late 1970s to the beginning of the 21st century, with a significant resonance cycle of 2–4a. The previous autumn IOD forced the Gill pattern in the upper troposphere by changing the Sea Surface Temperature Anomaly (SSTA) of the following summer in the Tropical Indian Ocean (TIO), resulting in the anticyclonic circulation anomaly in the Philippine Sea. It eventually alters the southwest monsoon near the South China Sea and the Philippine Sea. However, for a decade or more, as the Pacific Decadal Oscillation (PDO) phase turns from warm to cold and the relationship between El Niño–Southern Oscillation (ENSO) and IOD weakens, the impact of the previous autumn IOD on Indian Ocean SSTA in the summer of the following year weakens, resulting in a decrease in the correlation between the previous autumn IOD and SCSSM. The impact of the previous autumn’s Indian Ocean SSTA on the following year’s SCSSM is more considered in terms of the eastern Bay of Bengal and the Somali Sea basin. In addition, during the warm phase of PDO, the IOD of the previous autumn can cause abnormal summer precipitation in South China by strengthening or weakening the Western Pacific Subtropical High (WPSH) of the following summer, but there is no significant effect in the cold phase of PDO.

Fresh volcanic aerosols injected in the atmosphere during the volcano eruptive activity at the Cumbre Vieja area (La Palma, Canary Islands): Temporal evolution and vertical impact

For the first time in fifty years, the Cumbre Vieja volcanic area (La Palma, Canary Islands, Spain) erupted on September 19, 2021, giving birth to a new volcano. Fresh volcanic aerosols were continuously injected into the troposphere at different height levels, decreasing with time until the end of December 2021 (15 weeks duration). A wide set of different instrumentation was deployed all over the Island in order to evaluate the effects of the volcanic plumes on the atmosphere and the air quality. For the first time, a long-term study of the relative mass contribution and vertical impact of the volcanic components, ash and non-ash particles separately, during the eruptive activity was carried out in this work. In particular, a polarized Micro-Pulse Lidar (P-MPL) was deployed at Tazacorte (at around 8 km west from the volcano) in 24/7 operation from October 17, 2021 until the end of the volcano activity (11 weeks) for vertical monitoring of the volcanic particles. First, a statistical study of the mass conversion factors for mass concentration estimation of the volcanic (ash and non-ash) particles was performed by using the AERONET sun/sky-photometer dataset at Fuencaliente (at around 18 km south from the volcano). A representative mass conversion factor was obtained for ash and non-ash particles: 1.89 ± 0.53 and 0.31 ± 0.06 g m−2, respectively, with no dependence on time and optical depth. Second, these factors were used to calculate the ash and non-ash mass concentrations from P-MPL observations. Ash particles dominated 11% of the time and mostly until week 3 (i.e. week 7 from the volcanic eruption). Their mass concentration decreased by one order of magnitude: the relative ash mass contribution was 73 ± 18% with a total mass loading of 566 ± 281 mg m−2 at week 1, reducing gradually down to 38 ± 32% and 120 ± 49 mg m−2, respectively, at week 11. Layer-to-layer, it decreased with increasing layer-height; no ash was detected above 4 km at the end of the volcanic period. Third, in order to analyse the potential AERONET underestimation of the coarse mass conversion factor due to the 15 μm cutoff effect in the AERONET retrieval, two worst-case-scenarios (WCS) were examined, representing aged-like ash particles (WCS1, 4-μm radius) and fresh-like (WCS2, 10-μm radius). For both scenarios, the mass concentration of the volcanic plumes exceeded the first contamination level (>200 μg m−3, as defined by the UK Meteorological Office) up to 5–6 km height mostly during week 1 and up to 1–2 km until week 9. The extreme contamination level (>2000 μg m−3, aircraft flight limitations) was only exceeded from week 1 to week 6 under WCS2 conditions. This work infers a new long-term insight on the volcanic matter injected in the atmosphere with relevance for Air Quality issues and air traffic safety policies.

Automated Detection of coronaL MAss Ejecta origiNs for Space Weather AppliCations (ALMANAC)

AbstractAlerts of potentially hazardous coronal mass ejection (CME) are based on the detection of rapid changes in remote observations of the solar atmosphere. This paper presents a method that detects and estimates the central coordinates of CME eruptions in Extreme Ultraviolet data, with the dual aim of providing an early alert, and giving an initial estimate of the CME direction of propagation to a CME geometrical model. In particular, we plan to link the Automated detection of coronaL MAss ejecta origiNs for space weather AppliCations (ALMANAC) method to the CME detection and characterization module of the Space Weather Empirical Ensemble Package, which is a fully automated modular software package for operational space weather capability currently being developed for the UK Meteorological Office. In this work, ALMANAC is applied to observations by the Atmospheric Imaging Assembly (AIA) aboard the Solar Dynamics Observatory (SDO). As well as presenting the method, a proof of concept test is made on a limited set of data associated with 20 halo CMEs recorded by the Coordinated Data Analysis Workshop (CDAW) catalog near the activity maximum of solar cycle 24. SDO/AIA data for each event is processed at 6 min cadence to identify the on‐disk location and time of each CME. The absolute mean deviance between the ALMANAC and CDAW source event coordinates are within 37.05 ± 29.71 min and 11.01 ± 10.39°. These promising results give a solid foundation for future work, and will provide initial constraints to an automated CME alert and forecasting system.

An outlook multiple: The ontological multiplicity of the Met Office's 3‐month outlook

AbstractThis paper uses a material semiotic approach to explore how the ontological multiplicity of climate affects the communication of seasonal climate outlooks (SCO). The analysis is based on an ethnography of the UK Meteorological Office's (Met Office) 3‐month outlook, which predicts seasonal climate variability for the whole of the UK over the next 3 months. I advance geographical knowledge by critiquing a tendency among SCO providers to assume that stakeholders only treat the climate as a descriptive index of weather trends. Instead, I propose that the idea of a normal climate, which is central to this mode of communication, is an effect of materially diverse professional practices, such as running seasonal climate models, making contingency plans, or writing and publishing newspaper articles. These socio‐material practices enact multiple understandings, or ontologies, of what the climate is, which then shape how SCO are understood and used. The analysis identifies moments where ontologies of climate converge and diverge and discusses the effect that these moments have on the communication of the Met Office's 3‐month outlook. The paper concludes by assessing the theoretical significance of material semiotic approaches for geographical research into the multiplicity of climate, as well as the practical significance of this work for scientists who are involved in communicating climate information.

Intercomparison of joint bias correction methods for precipitation and flow from a hydrological perspective

The typical framework of the climate change impact assessment on water resources relies on plausible scenarios obtained from global climate models (GCMs) and hydrological models (HMs). Although regional climate models (RCMs) can better simulate local climate at a high-resolution grid, the direct use of model outputs from RCMs is not recommended as inputs for HMs due to systematic error. Existing studies have focused on the bias correction (BC) of climate model outputs without considering uncertainties/biases in hydrological modeling. In this regard, this study proposed an integrated framework that combines the BC of RCM precipitation and the simulated flow from the rainfall-runoff model, considering the underlying uncertainty in the parameters of the distribution function. The regional climate model, HadRM3, and the conceptual rainfall-runoff model, HYMOD, are employed. Observed daily precipitation, evapotranspiration, and discharge time series over the Thorverton catchment are compiled from the UK Meteorological Office. To examine the effectiveness of the combined strategy, four different BC approaches have been explored to reduce systematic biases in the flow simulated through the HMs using the RCM precipitation as input. Here, BCs of RCM and HM outputs have been applied under the condition that the bias-corrected ensembles should be within the range of the observed climate variability. The four BC models are considered: aathe RCM precipitation and flow are corrected by preserving their natural variabilities (Case-4). From a hydrological perspective, the Case-4 model showed the best performance among the four cases in terms of correcting the bias and the spread of the flow ensemble.

Comparison of Satellite-Based Sea Surface Temperature to In Situ Observations Surrounding Coral Reefs in La Parguera, Puerto Rico

Coral reefs are among the most biologically diverse ecosystems on Earth. In the last few decades, a combination of stressors has produced significant declines in reef expanse, with declining reef health attributed largely to thermal stresses. We investigated the correspondence between time-series satellite remote sensing-based sea surface temperature (SST) datasets and ocean temperature monitored in situ at depth in coral reefs near La Parguera, Puerto Rico. In situ temperature data were collected for Cayo Enrique and Cayo Mario, San Cristobal, and Margarita Reef. The three satellite-based SST datasets evaluated were NOAA’s Coral Reef Watch (CoralTemp), the UK Meteorological Office’s Operational SST and Sea Ice Analysis (OSTIA), and NASA’s Jet Propulsion Laboratory (G1SST). All three satellite-based SST datasets assessed displayed a strong positive correlation (>0.91) with the in situ temperature measurements. However, all SST datasets underestimated the temperature, compared with the in situ measurements. A linear regression model using the SST datasets as the predictor for the in situ measurements produced an overall offset of ~1 °C for all three SST datasets. These results support the use of all three SST datasets, after offset correction, to represent the temperature regime at the depth of the corals in La Parguera, Puerto Rico.

Phenological shuffling of major marine phytoplankton groups over the last six decades

AbstractAimPhytoplankton form the basis of the marine food web and are responsible for approximately 50% of the world's photosynthesis. Changes to their ecology are, therefore, important: here, we examined seasonal patterns in ocean phytoplankton abundance for 45 taxa over 59 years collected from circa 410,000 km of line‐transect sampling at temperate latitudes.LocationThe North Sea.MethodsFor our analysis we used plankton abundance data from the Continuous Plankton Recorder (CPR) survey, sea surface temperature measurements from the Hadley Centre, UK Meteorological Office and wind speed data from the International Comprehensive Ocean–Atmosphere Data Set, NOAA.ResultsWe found large differences in changes in the timing of peak abundance between the major phytoplankton groups. Late‐summer blooming dinoflagellates (n = 10 taxa) tended to show a large seasonal advancement, the timing of peak abundance for dinoflagellates as group advancing 39 days over these six decades. By contrast diatoms (n = 35) did not show any change as a group in their timing of peak abundance over the time series. Granger causality testing suggested a major driver of these phenological changes has been ocean warming in general but more specifically the rate of spring temperature rise as the most important factor. We also found differences in the timing of peak abundance of harmful algal bloom taxa, with some showing peak abundance earlier while others have moved later.Main conclusionsThere has been a fundamental transformation of the classic seasonal progression from blooms of diatoms to dinoflagellates, which lies at the heart of temperate marine food chains, as the classic bimodal diatom and dinoflagellate seasonal peaks are eroded to a more continuous, single, longer‐lasting phytoplankton peak. This phenological shuffling within and between major taxonomic groups is likely to have profound implications for the transfer of energy to higher trophic levels.

Mussels cope best when the going gets hot fast

![Figure][1] California mussels on the shore beneath the Hopkins Marine Station. Photo credit: Luc Moyen It's hard to deny; the planet is warming and it's getting hotter faster. According to the UK Meteorological Office, the top 10 warmest years on record have all occurred since 2002.

A mass participatory experiment provides a rich temporal profile of temperature response in spring onions.

Plants modulate their growth rates based on the environmental signals; however, it is difficult to experimentally test how natural temperature and light fluctuations affect growth, since realistic outdoor environments are difficult to replicate in controlled laboratory conditions, and it is expensive to conduct experiments in many environmentally diverse regions. In partnership with BBC Terrific Scientific, over 50 primary schools from around the UK grew spring onions outside of hydroponic growth chambers that they constructed. Over 2 weeks, students measured the height of the spring onions daily, while the hourly temperature and visibility data were determined for each school based on the UK Meteorological Office data. This rich time series data allowed us to model how plants integrate temperature and light signals to determine how much to grow, using techniques from functional data analysis. We determined that under nutrient‐poor hydroponic conditions, growth of spring onion is sensitive to even a few degrees change in temperature, and is most correlated with warm nighttime temperatures, high temperatures at the start of the experiment, and light exposure near the end of the experiment. We show that scientists can leverage schools to conduct experiments that leverage natural environmental variability to develop complex models of plant‐environment interactions.

Evaluating Changes in Soil Organic Matter with Climate Using CENTURY in England and Wales.

Soil organic C (SOC) dynamics are complex, and models have been developed for predicting future changes, validated using only individual site data. In this study, we used the CENTURY model to predict changes in SOC between 1978 and 2000 using input weather data for 1978 to 2000 from the UK Meteorological Office and soil property input data derived from the National Soil Inventory (NSI). The predicted changes in SOC from the model simulation were validated using the resampled NSI data for the period 1994 to 2000. The modeling results indicate that CENTURY gave unacceptable predictions of change for three specific soil types. When these were omitted from the accuracy assessment, model predictions were statistically acceptable for all ecosystem types with model efficiency (ME) decreasing in the order: seminatural grassland (ME = 0.63) > woodland (ME = 0.27) > arable (ME = 0.08) > managed grassland (ME = 0.02). When comparing the overall measured rates of change, CENTURY correctly predicted the direction but underpredicted the magnitude of change. Once this utility was established, CENTURY was used to predict nation-level climate change-induced changes in SOC with the UKCIP02 (UK Climate Impacts Program of 2002) scenarios for the 2020s, 2050s, and 2080s, each of which comprise four emissions scenarios. The modeling predictions suggest that the predicted changes between scenarios were small. However, within that, the greatest decrease (of 1.54% SOC) will be in seminatural grassland under the high emissions scenario. The future predicted pattern of change in SOC is greater in managed grassland (reduction of 0.27-0.39% SOC) than arable land (reduction of 0.03-0.05% SOC).

Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data

Abstract This study presents a hybrid numerical weather prediction model (NWP) and a Gaussian process regression (GPR) model for near surface wind speed prediction up to 72 h ahead using data partitioned on atmospheric stability class to improve model performance. NWP wind speed data from the UK meteorological office was corrected using a GPR model, where the data was subdivided using the atmospheric stability class calculated using the Pasquill-Gifford-Turner method based on observations at the time of prediction. The method was validated using data from 15 UK MIDAS (Met office Integrated Data Archive System) sites with a 9 month training and 3 month test period. Results are also shown for hub height wind speed prediction at one turbine. Additionally, power output is predicted for this turbine by translating hub height wind speed to power using a turbine power curve. While various forecasting methods exist, limited methods consider the impact of atmospheric stability on prediction accuracy. Therefore the method presented in this study gives a new way to improve wind speed predictions. Outputs show the GPR model improves forecast accuracy over the original NWP data, and consideration of atmospheric stability further reduces prediction errors. Comparing predicted power output to measured output reveals power predictions are also enhanced, demonstrating the potential of this novel wind speed prediction technique.

Comparison of Remotely-Sensed Sea Surface Temperature and Salinity Products With in Situ Measurements From British Columbia, Canada

Sea surface temperature (SST) and salinity (SSS) are essential variables at the ocean and atmosphere interface when considering risk factors for disease in farmed and wild fish stocks. Ecological research has witnessed a recent trend in use of digital and satellite technologies, including remote-sensing tools. We explored spatial coverage of remotely-sensed SST and SSS data and compared them with in situ measurements of water temperatures and salinity, which led to suggested adjustments to the remotely-sensed data for its use in aquaculture research. The in situ data were from farms and wild surveillance sites in coastal British Columbia, Canada, from 2003 to 2016. Concurrent SST and SSS values were extracted from remotely-sensed products and compared with 20,513 and 20,038 in situ records for water temperature and salinity, respectively, from 232 different sites. Among nine SST products evaluated, the UKMO OSTIA SST (UK Meteorological Office) had the highest retrieval, and highest concordance correlation coefficient (0.86), highest index of agreement (0.93), fewest missing values, and smallest mean and SD values for bias, when compared to in situ measurements. A mixed linear regression model with UKMO OSTIA SST as the predictor for in situ measurements estimated an adjustment coefficient of 0.89 °C for UKMO OSTIA SST. None of the three SSS products evaluated provided appropriate corresponding values for in situ sites, suggesting that spatial coverage for the study area is currently lacking. This study demonstrates that, among SST products, UKMO OSTIA SST is currently best suited for aquaculture studies in coastal BC. The near real-time availability of these data with the estimated adjustment would allow their use in forecast models, surveillance of pathogens, and the creation of risk maps.

Accuracy assessment of rainfall prediction by global models during the landfall of tropical cyclones in the North Indian Ocean

ABSTRACTThe rainfall prediction accuracy of seven state‐of‐the‐art operational models during landfall (LF) of tropical cyclones formed in the North Indian Ocean was assessed. The rainfall predicted by these models 1–3 days (24–72 h) before the LF day of tropical cyclones was compared with TRMM_3B42 rainfall data. Cyclone induced rainfall occurs along its track; thus rainfall within 600 km along the cyclone track was used for comparison. The error in cyclone track prediction affects the rainfall prediction. This has been taken care of by shifting the rainfall distribution over the cyclone best track positions provided by the India Meteorological Department. The validation statistics for the rainfall prediction accuracy of the models were computed by combining the forecasts generated by these seven models during LF of nine cyclones formed in the North Indian Ocean during 2010–2013. The skill of the models was analysed using different rainfall validation measures/statistics. Based on these validation statistics it was found that, of the seven global models, the models from four centres, namely the National Center for Environment Prediction, the European Centre for Medium‐range Weather Forecasts, the UK Meteorological Office and the Canadian Meteorological Centre, performed closer to the observations. However, there was no single model which was able to exhibit all the features of the observed rainfall.

SHORT-TERM AND MEDIUM-TERM FORECAST OF OIL SPILL TRAJECTORIES: APPLICATION TO LOCAL AND REGIONAL SCALES

ABSTRACTThis paper presents two methodologies to provide short-term and medium-term forecast of oil spill trajectories at local and regional scales. For short-term predictions (within 48 hours), a high-resolution operational oil spill forecast system is developed in Belfast Lough (Northern Ireland). Hydrodynamics are based on a Delft3D model which uses daily boundary conditions and meteorological forcing obtained from Copernicus Marine Environment Monitoring Service (CMEMS) and from the UK Meteorological Office. Downscaled currents and meteorological forecasts are used to provide short-term oil spill fate and trajectory predictions in the Lough using the oil spill numerical model TESEO. The system is integrated in a user-friendly web application that allows end users to launch the oil spill model both in case of pollution threat and for training purposes.For mid-term predictions (15–60 days), a stochastic methodology to provide probabilistic oil spill forecasts is presented and applied to the Bay of Biscay (Northern Spain). The method encompasses the following steps: 1) Classification of representative atmospheric patterns using principal component analysis and the k-means technique; 2) Setup of an autoregressive logistic model taking into account seasonality, covariates, long-term trends and autoregressive terms. In the case of an accident, we sample the evolution of the metocean conditions using the autoregressive model, which provides us with possible evolution patterns for these conditions during the forecasting period. These results are used to force the oil spill transport model TESEO allowing the characterization of trajectories in probabilistic terms.Drifting buoys released in Belfast Lough and observations reported during the Prestige accident have been used to validate the operational system and the medium-term forecasting methodologies.

A high-resolution operational forecast system for oil spill response in Belfast Lough

This paper presents a high-resolution operational forecast system for providing support to oil spill response in Belfast Lough. The system comprises an operational oceanographic module coupled to an oil spill forecast module that is integrated in a user-friendly web application. The oceanographic module is based on Delft3D model which uses daily boundary conditions and meteorological forcing obtained from COPERNICUS and from the UK Meteorological Office. Downscaled currents and meteorological forecasts are used to provide short-term oil spill fate and trajectory predictions at local scales. Both components of the system are calibrated and validated with observational data, including ADCP data, sea level, temperature and salinity measurements and drifting buoys released in the study area. The transport model is calibrated using a novel methodology to obtain the model coefficients that optimize the numerical simulations. The results obtained show the good performance of the system and its capability for oil spill forecast.

Stratospheric temperature changes during the satellite era

AbstractSatellite‐based layer average stratospheric temperature (T) climate data records (CDRs) now span more than three decades and so can elucidate climate variability associated with processes on multiple time scales. We intercompare and analyze available published T CDRs covering at least two decades, with a focus on Stratospheric Sounding Unit (SSU) and Microwave Sounding Unit (MSU) CDRs. Recent research has reduced but not eliminated discrepancies between SSU CDRs developed by NOAA and the UK Meteorological Office. The MSU CDRs from NOAA and Remote Sensing Systems are in closer agreement than the CDR from the University of Alabama in Huntsville. The latter has a previously unreported inhomogeneity in 2005, revealed by an abrupt increase in the magnitude and spatial variability of T anomaly differences between CDRs. Although time‐varying biases remain in both SSU and MSU CDRs, multiple linear regression analyses reveal consistent solar, El Niño–Southern Oscillation (ENSO), quasi‐biennial oscillation, aerosol, and piecewise‐linear trend signals. Together, these predictors explain 80 to 90% of the variance in the near‐global‐average T CDRs. The most important predictor variables (in terms of percent explained variance in near‐global‐average T) for lower stratospheric T measured by MSU are aerosols, solar variability, and ENSO. Trends explain the largest percentage of variance in observations from all three SSU channels. In MSU and SSU CDRs, piecewise‐linear trends, with a 1995 break point, indicate cooling during 1979–1994 but no trend during 1995–2013 for MSU and during 1995–2005 for SSU. These observational findings provide a basis for evaluating climate model simulations of stratospheric temperature during the past 35 years.