Future wave climate over the west-European shelf seas

In this paper, we investigate changes in the wave climate of the west-European shelf seas under global warming scenarios. In particular, climate change wind fields corresponding to the present (control) time-slice 1961–2000 and the future (scenario) time-slice 2061–2100 are used to drive a wave generation model to produce equivalent control and scenario wave climate. Yearly and seasonal statistics of the scenario wave climates are compared individually to the corresponding control wave climate to identify relative changes of statistical significance between present and future extreme and prevailing wave heights. Using global, regional and linked global–regional wind forcing over a set of nested computational domains, this paper further demonstrates the sensitivity of the results to the resolution and coverage of the forcing. It suggests that the use of combined forcing from linked global and regional climate models of typical resolution and coverage is a good option for the investigation of relative wave changes in the region of interest of this study. Coarse resolution global forcing alone leads to very similar results over regions that are highly exposed to the Atlantic Ocean. In contrast, fine resolution regional forcing alone is shown to be insufficient for exploring wave climate changes over the western European waters because of its limited coverage. Results obtained with the combined global–regional wind forcing showed some consistency between scenarios. In general, it was shown that mean and extreme wave heights will increase in the future only in winter and only in the southwest of UK and west of France, north of about 44–45° N. Otherwise, wave heights are projected to decrease, especially in summer. Nevertheless, this decrease is dominated by local wind waves whilst swell is found to increase. Only in spring do both swell and local wind waves decrease in average height.     

Climate extremes in South Western Siberia: past and future

In this study, the temporal and spatial trends of ten climate extreme indices were computed based on observed daily precipitation and on daily maximum and minimum temperatures at 26 weather stations in South Western Siberia during the period 1969–2011 and, based on projected daily maximum and minimum temperatures, during 2021–2050. The Mann–Kendall test was employed to analyze the temporal trend and a combination of multiple linear regressions and semivariogram functions were used to evaluate the regional spatial trends and the local spatial variability of climate extremes, respectively. The results show that the temperature-based climate extremes increase at a 0.05 significance level while none of the precipitation-based climate extremes did. Spatially, dominant gradients are observed along latitude: The northern taiga vegetation zone experiences a colder and wetter climate while the southern forest steppe zone is drier and hotter. Over time, a tendency towards homogenization of the regional climate is observed through a decrease of the spatial variability for most climate extreme indices. In the future, the most intense changes are anticipated for the bio-climate indicators “growing season length” and “growing degree days” in the north, while the warming indicators, “warm day” and “warm night” are expected to be high to the south.     

What dominates sea level at the coast: a case study for the Gulf of Guinea

Sea level variations and extreme events are a major threat for coastal zones. This threat is expected to worsen with time because low-lying coastal areas are expected to become more vulnerable to flooding and land loss as sea level rises in response to climate change. Sea level variations in the coastal ocean result from a combination of different processes that act at different spatial and temporal scales. In this study, the relative importance of processes causing coastal sea level variability at different time-scales is evaluated. Contributions from the altimetry-derived sea-level (including the sea level rise due to the ocean warming and land ice loss in response to climate change), dynamical atmospheric forcing induced sea level (surges), wave-induced run-up and set-up, and astronomical tides are estimated from observational datasets and reanalyses. As these processes impact the coast differently, evaluating their importance is essential for assessment of the local coastline vulnerability. A case study is developed in the Gulf of Guinea over the 1993–2012 period. The leading contributors to sea level variability off Cotonou differ depending on the time-scales considered. The trend is largely dominated by processes included in altimetric data and to a lesser extent by swell-waves run-up. The latter dominates interannual variations. Swell-waves run-up and tides dominate subannual variability. Extreme events are due to the conjunction of high tides and large swell run-up, exhibiting a clear seasonal cycle with more events in boreal summer and a trend mostly related to the trend in altimetric-derived sea-level.     

Large scale climate and rainfall seasonality in a Mediterranean Area: Insights from a non‐homogeneous Markov model applied to the Agro‐Pontino plain

In the context of climate change and variability, there is considerable interest in how large scale climate indicators influence regional precipitation occurrence and its seasonality. Seasonal and longer climate projections from coupled ocean–atmosphere models need to be downscaled to regional levels for hydrologic applications, and the identification of appropriate state variables from such models that can best inform this process is also of direct interest. Here, a Non‐Homogeneous Hidden Markov Model (NHMM) for downscaling daily rainfall is developed for the Agro‐Pontino Plain, a coastal reclamation region very vulnerable to changes of hydrological cycle. The NHMM, through a set of atmospheric predictors, provides the link between large scale meteorological features and local rainfall patterns. Atmospheric data from the NCEP/NCAR archive and 56‐years record (1951–2004) of daily rainfall measurements from 7 stations in Agro‐Pontino Plain are analyzed. A number of validation tests are carried out, in order to: 1) identify the best set of atmospheric predictors to model local rainfall; 2) evaluate the model performance to capture realistically relevant rainfall attributes as the inter‐annual and seasonal variability, as well as average and extreme rainfall patterns. Validation tests show that the best set of atmospheric predictors are the following: mean sea level pressure, temperature at 1000 hPa, meridional and zonal wind at 850 hPa and precipitable water, from 20°N to 80°N of latitude and from 80°W to 60°E of longitude. Furthermore, the validation tests show that the rainfall attributes are simulated realistically and accurately. The capability of the NHMM to be used as a forecasting tool to quantify changes of rainfall patterns forced by alteration of atmospheric circulation under climate change and variability scenarios is discussed.     

Spatial characteristics of ocean surface waves

The spatial variability of open ocean wave fields on scales of O (10km) is assessed from four different data sources: TerraSAR-X SAR imagery, four drifting SWIFT buoys, a moored waverider buoy, and WAVEWATCH III^? model runs. Two examples from the open north-east Pacific, comprising of a pure wind sea and a mixed sea with swell, are given. Wave parameters attained from observations have a natural variability, which decreases with increasing record length or acquisition area. The retrieval of dominant wave scales from point observations and model output are inherently different to dominant scales retrieved from spatial observations. This can lead to significant differences in the dominant steepness associated with a given wave field. These uncertainties have to be taken into account when models are assessed against observations or when new wave retrieval algorithms from spatial or temporal data are tested. However, there is evidence of abrupt changes in wave field characteristics that are larger than the expected methodological uncertainties.     

Precipitation in the Pearl River basin, South China: scaling, regional patterns, and influence of large-scale climate anomalies

This study examines the temporal patterns of precipitation and the influence of large-scale climate anomalies in the Pearl River basin (South China), with particular focus on sub-basin scale. Three popular data analysis techniques are employed: (1) wavelet analysis; (2) principal component analysis (PCA); and (3) rank correlation method. With due consideration to hydrologic factors, water resources activities, and large-scale climate data, the entire basin is divided into ten sub-basins and the analysis is performed on monthly data. The wavelet analysis reveals discernible differences in temporal scales of fluctuation embedded in the monthly precipitation anomalies over the basin. The PCA delineates three coherent regions exhibiting similar distribution of variability across scales. Analysis of linkages between precipitation and teleconnection patterns using cross-wavelet transform and wavelet coherence reveals that the dominant variabilities of precipitation are essentially depicted by the Indian Ocean Dipole (IOD), especially for the central and eastern part of the Pearl River basin. On the influence of El Niño-Southern Oscillation (ENSO) signal on precipitation, more significant correlation is detected for the eastern part of the basin, long-term relationships (within 4–8 years band) are found for the western part of the basin, while the central part seems to be acting as a transition zone. Rank correlations of scale-averaged wavelet power between regional precipitation and climate indices for the dominant low-frequency variability band (0.84–8.40 years) provide further support to the different precipitation-climate relationships for different regions over the basin. The present results provide valuable information towards: (1) improving predictions of extreme hydroclimatic events in the Pearl River basin, based on their relationships with IOD or ENSO; and (2) devising better adaptation and mitigation strategies under a future changing climate.     

Temporal change of spatial heterogeneity and its effect on regional trend of annual precipitation heterogeneity indices

A statistical test on climate and hydrological series from different spatial resolution could obtain different regional trend due to spatial heterogeneity and its temporal variability. In this study, annual series of the precipitation heterogeneity indices of concentration index (CI) and the number of wet days (NW) along with annual total amount of precipitation were calculated based on at‐site daily precipitation series during 1962–2011 in the headwater basin of the Huaihe River, China. The regional trends of the indices were first detected based on at‐site series by using the aligned and intrablock methods, and field significance tests that consider spatial heterogeneity over sites. The detected trends were then compared with the trends of the regional index series derived from daily areal average precipitation (DAAP), which averages at‐site differences and thus neglects spatial heterogeneity. It was found that the at‐site‐based regional test shows increasing trends of CI and NW in the basin, which follows the test on individual sites that most of sites were characterized by increasing CI and NW. However, the DAAP‐derived regional series of CI and NW were tested to show a decreasing trend. The disparity of the regional trend test on at‐site‐based regional series and the DAAP‐derived regional series arises from a temporal change of the spatial heterogeneity, which was quantified by the generalized additive models for location, scale, and shape. This study highlights that compared with averaging indices, averaging at‐site daily precipitation could lead to an error in the regional trend inference on annual precipitation heterogeneity indices. More attention should be paid to temporal variability in spatial heterogeneity when data at large scales are used for regional trend detection on hydro‐meteorological events associated with intra‐annual heterogeneity.     

A high-resolution satellite-derived sea surface temperature climatology for the western North Atlantic Ocean

Long-term and high-resolution (∼1.2 km) satellite-derived sea surface temperature (SST) fields of a monthly mean time series for the 1985–1999 period, and a daily climatology have been calculated for the North West Atlantic Ocean. The SST fields extend from 78°W to 41°W in longitude, and 30°N to 56°N in latitude, encompassing the region off Cape Hatteras, North Carolina, to the southern Labrador Sea. The monthly mean time series, consists of 180 cloud-masked monthly mean SST fields, derived from a full-resolution NOAA/NASA Pathfinder SST data set for the 1985–1999 period. The satellite-derived monthly mean SST fields, as compared with in situ monthly mean near-surface ocean temperatures from buoys located in the western North Atlantic, yield an overall RMS difference of 1.15 °C. The daily climatology, which consists of 365 fields, was derived by applying a least-squares harmonic regression technique on the monthly mean SST time series for the full study period. The monthly mean and daily climatological SST fields will be useful for studying inter-annual variability related to climate variability of SST over the study domain.     

Coupled rainfall and water vapour stable isotope time series reveal tropical atmospheric processes on multiple timescales

High-frequency stable isotope data are useful for validating atmospheric moisture circulation models and provide improved understanding of the mechanisms controlling isotopic compositions in tropical rainfall. Here, we present a near-continuous 6-month record of O- and H-isotope compositions in both water vapour and daily rainfall from Northeast Australia measured by laser spectroscopy. The data set spans both wet and dry seasons to help address a significant data and knowledge gap in the southern hemisphere tropics. We interpret the isotopic records for water vapour and rainfall in the context of contemporaneous meteorological observations. Surface air moisture provided near-continuous tracking of the links between isotopic variations and meteorological events on local to regional spatial scales. Power spectrum analysis of the isotopic variation showed a range of significant periodicities, from hourly to monthly scales, and cross-wavelet analysis identified significant regions of common power for hourly averaged water vapour isotopic composition and relative humidity, wind direction, and solar radiation. Relative humidity had the greatest subdiurnal influence on isotopic composition. On longer timescales (weeks to months), isotope variability was strongly correlated with both wind direction and relative humidity. The high-frequency records showed diurnal isotopic variations in O- and H-isotope compositions due to local dew formation and, for deuterium excess, as a result of evapotranspiration. Several significant negative isotope anomalies on a daily scale were associated with the activity of regional mesoscale convective systems and the occurrence of two tropical cyclones. Calculated air parcel back trajectories identified the predominant moisture transport paths from the Southwest Pacific Ocean, whereas moisture transport from northerly directions occurred mainly during the wet season monsoonal airflow. Water vapour isotope compositions reflected the same meteorological events as recorded in rainfall isotopes but provided much more detailed and continuous information on atmospheric moisture cycling than the intermittent isotopic record provided by rainfall. Improved global coverage of stable isotope data for atmospheric water vapour is likely to improve simulations of future changes to climate drivers of the hydrological cycle.     

The hydrological response of surface water to recent climate variability: A remote sensing case study from the central tropical Pacific

For small tropical islands with limited freshwater resources, understanding how island hydrology is influenced by regional climate is important, considering projected hydroclimate and sea level changes as well as growing populations dependent on limited groundwater resources. However, the relationship between climate variability and hydrologic variability for many tropical islands remains uncertain due to local hydroclimatic data scarcity. Here, we present a case study from Kiritimati, Republic of Kiribati (2°N, 157°W), utilizing the normalized difference vegetation index to investigate variability in island surface water area, an important link between climate variability and groundwater storage. Kiritimati surface water area varies seasonally, following wet and dry seasons, and interannually, due to hydroclimate variability associated with the El Niño/Southern Oscillation. The NIÑO3.4 sea surface temperature index, satellite‐derived precipitation, precipitation minus evaporation, and local sea level all had significant positive correlations with surface water area. Lagged correlations show sea level changes and precipitation influence surface water area up to 6 months later. Differences in the timing of surface water area changes and variable climate‐surface water area correlations in island subregions indicate that surface hydrology on Kiritimati is not uniform in response to climate variations. Rather, the magnitude of the ocean–atmosphere anomalies and island–ocean connectivity determine the extent to which sea level and precipitation control surface water area. The very strong 2015–2016 El Niño event led to the largest surface water area measured in the 18‐year data set. Surface water area decreased to pre‐event values in a similarly rapid manner (<6 months) after both the very strong 2015–2016 event and the 2009–2010 moderate El Niño event. Future changes in the frequency and amplitude of interannual hydroclimate variability as well as seasonal duration will thus alter surface water coverage on Kiritimati, with implications for freshwater resources, flooding, and drought.     

Storm observations by remote sensing and influences of gustiness on ocean waves and on generation of rogue waves

The impact of the gustiness on surface waves under storm conditions is investigated with focus on the appearance of wave groups with extreme high amplitude and wavelength in the North Sea. During many storms characterized by extremely high individual waves measured near the German coast, especially in cold air outbreaks, the moving atmospheric open cells are observed by optical and radar satellites. According to measurements, the footprint of the cell produces a local increase in the wind field at sea surface, moving as a consistent system with a propagation speed near to swell wave-traveling speed. The optical and microwave satellite data are used to connect mesoscale atmospheric turbulences and the extreme waves measured. The parameters of open cells observed are used for numerical spectral wave modeling. The North Sea with horizontal resolution of 2.5?km and with focus on the German Bight was simulated. The wind field “storm in storm,” including moving organized mesoscale eddies with increased wind speed, was generated. To take into account the rapid moving gust structure, the input wind field was updated each 5?min. The test cases idealized with one, two, and four open individual cells and, respectively, with groups of open cells, with and without preexisting sea state, as well the real storm conditions, are simulated. The model results confirm that an individual-moving open cell can cause the local significant wave height increase in order of meters within the cell area and especially in a narrow area of 1–2?km at the footprint center of a cell (the cell's diameter is 40–90?km). In a case of a traveling individual open cell with 15?m·s^?1 over a sea surface with a preexisting wind sea of and swell, a local significant wave height increase of 3.5?m is produced. A group of cells for a real storm condition produces a local increase of significant wave height of more than 6?m during a short time window of 10–20?min (cell passing). The sea surface simulation from modeled wave spectra points out the appearance of wave groups including extreme individual waves with a period of about 25?s and a wavelength of more than 350?m under the cell's footprint. This corresponds well with measurement of a rogue wave group with length of about 400?m and a period of near 25?s. This has been registered at FiNO-1 research platform in the North Sea during Britta storm on November 1, 2006 at 04:00 UTC. The results can explain the appearance of rogue waves in the German Bight and can be used for ship safety and coastal protection. Presently, the considered mesoscale gustiness cannot be incorporated in present operational wave forecasting systems, since it needs an update of the wind field at spatial and temporal scales, which is still not available for such applications. However, the scenario simulations for cell structures with appropriate travel speed, observed by optical and radar satellites, can be done and applied for warning messages.     

Long-term statistics of potentially hazardous sea states in the North Sea 1958–2014

Significant wave height and mean wave period are two of the most commonly used parameters to describe wave climate, wave climate variability, and their potential long-term changes. While these parameters are generally useful to characterize the distribution of waves within a given sea state, they provide less information about potentially high-risk situations. Over the recent years, a number of criteria were suggested that are considered to better characterize high-risk situations and which could bear a potential for the development of safety warning indices. Based on a multi-decadal high-resolution wind-wave hindcast, a climatology of such parameters is developed for the North Sea covering the years 1958–2014. More specifically, average conditions, inter-annual variability and long-term changes for unusually steep, rapidly developing and crossing sea states are considered. Generally, there are pronounced spatial variations in the frequency of such sea states, while over time, there is some seasonal and inter-annual variability but no substantial long-term trend could be identified.     

Improving drought predictability in Arkansas using the ensemble PDSI forecast technique

Drought prediction is important for improved water resources management and agriculture planning. Although Arkansas has suffered severe droughts and economic loss in recent years, no significant study has been done. This study proposes a local nonparametric autoregressive model with designed stochastic residual-resampling approach to produce ensemble drought forecasts with associated confidence. The proposed model utilizes historical climate records, including drought indices, temperature, and precipitation to improve the quality of the short-term forecast of drought indices. Monthly forecasts of Palmer Drought Severity Index (PDSI) in Arkansas climate divisions show remarkable skills with 2–3 month lead-time based on selected performance measure such as, Normalized Root Mean Square Error (NRMSE) and the Kuiper Skill Score (KSS). Rank histograms also show that the model captures the natural variability very well in the produced drought forecasts. The incorporation of categorical long-term precipitation prediction significantly enhances the performance of the monthly drought forecasts.     

Pressure effects on regional mean sea level trends in the German Bight in the twenty-first century

The effect of large-scale atmospheric pressure changes on regional mean sea level projections in the German Bight in the twenty-first century are considered. A developed statistical model is applied to climate model data of sea level pressure for the twenty-first century to assess the potential contribution of large-scale atmospheric changes to future sea level changes in the German Bight. Using 78 experiments, an ensemble mean of 1.4-cm rise in regional mean sea level is estimated until the end of the twenty-first century. Changes are somewhat higher for realisations of the special report on emission scenarios (SRES) A1B and A2, but generally do not exceed a few centimeters. This is considerably smaller than the changes expected from steric and self-gravitational effects. Large-scale changes in sea level pressure are thus not expected to provide a substantial contribution to twenty-first century sea level changes in the German Bight.     

Coral bleaching indices and thresholds for the Florida Reef Tract, Bahamas, and St. Croix, US Virgin Islands

It is well established that elevated sea temperatures cause widespread coral bleaching, yet confusion lingers as to what facet of extreme temperatures is most important. Utilizing long-term in situ datasets, we calculated nine thermal stress indices and tested their effectiveness at segregating bleaching years a posteriori for multiple reefs on the Florida Reef Tract. The indices examined represent three aspects of thermal stress: (1) short-term, acute temperature stress; (2) cumulative temperature stress; and (3) temperature variability. Maximum monthly sea surface temperature (SST) and the number of days >30.5 °C were the most significant; indicating that cumulative exposure to temperature extremes characterized bleaching years. Bleaching thresholds were warmer for Florida than the Bahamas and St. Croix, US Virgin Islands reflecting differences in seasonal maximum SST. Hind-casts showed that monthly mean SST above a local threshold explained all bleaching years in Florida, the Bahamas, and US Virgin Islands.     

Effect of geomagnetic and volcanic activity on the El Nino and La Nina phenomena

The monthly values of the southern atmospheric oscillation indices (SOI), the corresponding values of the Nino-3.4 index, the data on the onsets of intense volcanic eruptions from 1870 to 2002, the daily values of the Ap and AE indices and the IMF B _z component, and the data on cloudiness and wind characteristics at 14 Antarctic stations have been considered. The beginning of the warm El Nino current is observed after an increase in the amplitude of the Ap magnetic indices, which continues for more than five months. The beginning of the cold period of the La Nina southern atmospheric oscillation is as a rule related to a decrease in Ap. A change in atmospheric transparency caused by volcanic eruptions is often followed by the beginning of the cold period of the southern atmospheric oscillation (ENSO). A change in the wind system in the Antarctic Regions, related to a change in the temperature balance caused by variations in the solar wind parameters in the winter season, promotes a short-term disturbance of the circumpolar vortex and the beginning of the El Nino warm period.     

Regionalization of precipitation characteristics in the Canadian Prairie Provinces using large-scale atmospheric covariates and geophysical attributes

Observed data at most stations are often inadequate to obtain reliable estimates of many hydro-meteorological variables that not only define water availability across a region but also the vulnerability of social infrastructure to climatic extremes. To overcome this, data from neighboring sites with similar statistical characteristics are often pooled. The pooling process is based on partitioning of a larger region into smaller sub-regions with homogeneous features of interest. The established approaches rely heavily on statistics computed from observed precipitation data rather than the covariates that play a significant role in modulating the regional and local climate patterns at various temporal and spatial scales. In this study, a new approach for identifying homogeneous regions for regionalization of precipitation characteristics is proposed for the Canadian Prairie Provinces. This approach incorporates information about large-scale atmospheric covariates, teleconnection indices and geographical site attributes that impact spatial patterns of precipitation in order to delineate homogeneous precipitation regions through combined use of multivariate approaches—principal component analysis, canonical correlation analysis and fuzzy C-means clustering. Results of the analyses suggest that the study area can be partitioned into five homogeneous regions. These partitions are validated independently for homogeneity using statistics computed from monthly and seasonal precipitation totals, and seasonal extremes from a network of observation stations. Furthermore, based on the identified regions, precipitation magnitude-frequency relationships of warm and cold season single- and multi-day precipitation extremes, developed through regional frequency analysis, are mapped spatially. Such estimates are important for numerous water resources related activities.     

Coastal and global sea level change

Both coastal and global mean sea level rise by about 3.0 ± 0.5 mm/year from January 1993 to December 2004. Over shorter intervals the coastal sea level rises faster and over longer intervals slowly than the global mean, which trend is almost constant for each interval and is equal to 2.9 ± 0.5 mm/year in 1993–2008. The different trends are due to the higher interannual variability of coastal sea level, caused by the sea level regional variability, that is further averaged out when computing the global mean.Coastal sea level rise is well represented by a selected set of 267 stations of the Permanent Service for Mean Sea Level and by the corresponding co-located altimeter points. Its departure from coastal sea level computed from satellite altimetry in a 150 km distance from coast, dominated by a large rise in the Eastern Pacific, is due to the regional interannual variability.Regionally the trends of the coastal and open-ocean sea level variability are in good agreement and the main world basins have a positive averaged trend. The interannual variability is highly correlated with the El Nino Southern Oscillation (SO) and the North Atlantic Oscillation (NAO) climatic indices over both the altimeter period and the interval 1950–2001. Being the signal of large scale a small number of stations with good spatial coverage is needed. The reconstruction of the interannual variability using the spatial pattern from altimetry and the temporal patterns from tide gauges correlated to NAO and SOI restitutes about 50% of the observed interannual variability over 1993–2001.     

Imprints of wave climate and mean sea level variations in the dynamics of a coastal spit over the last 250 years: Cap Ferret,SW France

In coastal areas, sea level rise (SLR) and changing wave climates are expected to be the main oceanic drivers of shoreline adjustments. These drivers have been shown to vary on a wide spectrum of spatial and temporal scales. Nonetheless, a general rule about how this variability impacts global shorelines remains to be articulated. Here, we discuss the impacts of wave climate changes and SLR on the evolution of a barrier spit–inlet system over the last 250 years. The distal end of the Cap Ferret barrier spit, SW France, has undergone large-scale oscillations that were well correlated with variations of the decadal average of the winter North Atlantic Oscillation (NAO) index. The local wave climate hindcast supports that increased alongshore wave energy fluxes associated with the positive phase of the NAO were responsible for the updrift retreat of the spit. By opposition, the spit has elongated downdrift when waves were less energetic and more shore normal, as during the negative phase of the NAO. In addition, lower rates of SLR appeared to be necessary for the spit to develop, as higher rates of SLR very likely forced the adjacent inlet to enlarge, at the expense of the spit. These results should help to predict and detect coastal adjustments driven by climate change and by climate variability. © 2019 John Wiley & Sons, Ltd.