Drivers of mean climate change around the Netherlands derived from CMIP5

For the construction of regional climate change scenarios spanning a relevant fraction of the spread in climate model projections, an inventory of major drivers of regional climate change is needed. For the Netherlands, a previous set of regional climate change scenarios was based on the decomposition of local temperature/precipitation changes into components directly linked to the level of global warming, and components related to changes in the regional atmospheric circulation. In this study this decomposition is revisited utilizing the extensive modelling results from the CMIP5 model ensemble in support for the 5th IPCC assessment. Rather than selecting a number of GCMs based on performance metrics or relevant response features, a regression technique was developed to utilize all available model projections. The large number of projections allows a quantification of the separate contributions of emission scenarios, systematic model responses and natural variability to the total likelihood range. Natural variability plays a minor role in modelled differences in the global mean temperature response, but contributes for up to 50 % to the range of mean sea level pressure responses and local precipitation. Using key indicators (“steering variables”) for the temperature and circulation response, the range in local seasonal mean temperature and precipitation responses can be fairly well reproduced.     

Exploring high-end scenarios for local sea level rise to develop flood protection strategies for a low-lying delta—the Netherlands as an example

Sea level rise, especially combined with possible changes in storm surges and increased river discharge resulting from climate change, poses a major threat in low-lying river deltas. In this study we focus on a specific example of such a delta: the Netherlands. To evaluate whether the country’s flood protection strategy is capable of coping with future climate conditions, an assessment of low-probability/high-impact scenarios is conducted, focusing mainly on sea level rise. We develop a plausible high-end scenario of 0.55 to 1.15 m global mean sea level rise, and 0.40 to 1.05 m rise on the coast of the Netherlands by 2100 (excluding land subsidence), and more than three times these local values by 2200. Together with projections for changes in storm surge height and peak river discharge, these scenarios depict a complex, enhanced flood risk for the Dutch delta.     

Changes in the occurrence of storm surges around the United Kingdom under a future climate scenario using a dynamic storm surge model driven by the Hadley Centre climate models

 A potential consequence of climate change is an alteration of the frequency of extreme coastal storm surge events. It is these extreme events which, from an impacts point of view, will be of more concern than the slow inundation of coastal areas by century scale changes in mean sea level. In this study, a 35 km resolution storm surge model of the North west European continental shelf region has been driven by winds and pressures from the Hadley Centre nested regional climate model. Simulations of both present day and future climate (the end of the twentyfirst century) have been performed. The results suggest that, in addition to the effect of rising mean sea level, at many locations around the United Kingdom coastline future changes in local meteorology will lead to further significant changes in the return periods of extreme storm surge events. At most sites, this meteorologically forced change represents a reduction in return period. Received: 18 September 2000 / Accepted: 8 February 2001     

Baltic Sea climate in the late twenty-first century: a dynamical downscaling approach using two global models and two emission scenarios

A regional ocean circulation model was used to project Baltic Sea climate at the end of the twenty-first century. A set of four scenario simulations was performed utilizing two global models and two forcing scenarios. To reduce model biases and to spin up future salinity the so-called Δ-change approach was applied. Using a regional coupled atmosphere–ocean model 30-year climatological monthly mean changes of atmospheric surface data and river discharge into the Baltic Sea were calculated from previously conducted time slice experiments. These changes were added to reconstructed atmospheric surface fields and runoff for the period 1903–1998. The total freshwater supply (runoff and net precipitation) is projected to increase between 0 and 21%. Due to increased westerlies in winter the annual mean wind speed will be between 2 and 13% larger compared to present climate. Both changes will cause a reduction of the average salinity of the Baltic Sea between 8 and 50%. Although salinity in the entire Baltic might be significantly lower at the end of the twenty-first century, deep water ventilation will very likely only slightly change. The largest change is projected for the secondary maximum of sea water age within the halocline. Further, the average temperature will increase between 1.9 and 3.2°C. The temperature response to atmospheric changes lags several months. Future annual maximum sea ice extent will decrease between 46 and 77% in accordance to earlier studies. However, in contrast to earlier results in the warmest scenario simulation one ice-free winter out of 96 seasons was found. Although wind speed changes are uniform, extreme sea levels may increase more than the mean sea level. In two out of four projections significant changes of 100-year surge heights were found.     

统计降尺度法对华北地区未来区域气温变化情景的预估

迄今为止，大部分海气耦合气候模式（AOGCM）的空间分辨率还较低，很难对区域尺度的气候变化情景做合理的预测。降尺度法已广泛用于弥补AOGCM在这方面的不足。作者采用统计降尺度方法对1月和7月华北地区49个气象观测站的未来月平均温度变化情景进行预估。采用的统计降尺度方法是主分量分析与逐步回归分析相结合的多元线性回归模型。首先，采用1961～2000年的 NCEP再分析资料和49个台站的观测资料建立月平均温度的统计降尺度模型，然后把建立的统计降尺度模型应用于HadCM3 SRES A2 和 B2 两种排放情景， 从而生成各个台站1950～2099年1月份和7月份温度变化情景。结果表明:在当前气候条件下，无论1月还是7月，统计降尺度方法模拟的温度与观测的温度有很好的一致性，而且在大多数台站，统计降尺度模拟气温与观测值相比略微偏低。对于未来气候情景的预估方面，无论1月还是7月，也无论是HadCM3 SRES A2 还是B2排放情景驱动统计模型，结果表明大多数的站点都存在温度的明显上升趋势，同时7月的上升趋势与1月相比偏低。     

Statistical downscaling of sea-surface wind over the Peru�CChile upwelling region: diagnosing the impact of climate change from the IPSL-CM4 model

The key aspect of the ocean circulation off Peru?CChile is the wind-driven upwelling of deep, cold, nutrient-rich waters that promote a rich marine ecosystem. It has been suggested that global warming may be associated with an intensification of upwelling-favorable winds. However, the lack of high-resolution long-term observations has been a limitation for a quantitative analysis of this process. In this study, we use a statistical downscaling method to assess the regional impact of climate change on the sea-surface wind over the Peru?CChile upwelling region as simulated by the global coupled general circulation model IPSL-CM4. Taking advantage of the high-resolution QuikSCAT wind product and of the NCEP reanalysis data, a statistical model based on multiple linear regressions is built for the daily mean meridional and zonal wind at 10?m for the period 2000?C2008. The large-scale 10?m wind components and sea level pressure are used as regional circulation predictors. The skill of the downscaling method is assessed by comparing with the surface wind derived from the ERS satellite measurements, with in situ wind observations collected by ICOADS and through cross-validation. It is then applied to the outputs of the IPSL-CM4 model over stabilized periods of the pre-industrial, 2?×?CO₂ and 4?×?CO₂ IPCC climate scenarios. The results indicate that surface along-shore winds off central Chile (off central Peru) experience a significant intensification (weakening) during Austral winter (summer) in warmer climates. This is associated with a general decrease in intra-seasonal variability.     

A high-resolution 44-year atmospheric hindcast for the Mediterranean Basin: contribution to the regional improvement of global reanalysis

A 44-year (1958–2001) high-resolution atmospheric hindcast for the whole Mediterranean Basin was performed within the EU-funded Hindcast of Dynamic Processes of the Ocean and Coastal Areas of Europe (HIPOCAS) Project. The long-term hindcasted data set, which comprises several atmospheric parameters at different levels, was produced by means of dynamical downscaling from the NCEP/NCAR global reanalysis using the atmospheric limited area model REMO. The REMO hindcast has been exhaustively validated. On that score, various hindcasted surface parameters, such as 10-m wind field, 2-m temperature and mean sea level pressure, have been compared to satellite data (ERS-1/2 scatterometer) and in-situ measurements from offshore stations. In addition, two ocean models (waves and sea level) have been forced with REMO hindcasted fields (mean sea level pressure and 10-m wind field). The validation of these ocean runs, performed through comparisons of simulated waves and sea level with oceanographic measurements, allows to evaluate "indirectly" the quality of the REMO hindcasted data used as atmospheric forcing. Once the quality of the hindcasted data was verified, the efficiency of the regional enhancement performed through dynamical downscaling on the NCEP global reanalysis was assessed. The regional improvement was evaluated through comparisons of REMO and NCEP performance in reproducing observations. The important improvement obtained in the characterization of extreme wind events is particularly remarkable.     

Evaluating the performance of CMIP3 and CMIP5 global climate models over the north-east Atlantic region

One of the main sources of uncertainty in estimating climate projections affected by global warming is the choice of the global climate model (GCM). The aim of this study is to evaluate the skill of GCMs from CMIP3 and CMIP5 databases in the north-east Atlantic Ocean region. It is well known that the seasonal and interannual variability of surface inland variables (e.g. precipitation and snow) and ocean variables (e.g. wave height and storm surge) are linked to the atmospheric circulation patterns. Thus, an automatic synoptic classification, based on weather types, has been used to assess whether GCMs are able to reproduce spatial patterns and climate variability. Three important factors have been analyzed: the skill of GCMs to reproduce the synoptic situations, the skill of GCMs to reproduce the historical inter-annual variability and the consistency of GCMs experiments during twenty-first century projections. The results of this analysis indicate that the most skilled GCMs in the study region are UKMO-HadGEM2, ECHAM5/MPI-OM and MIROC3.2(hires) for CMIP3 scenarios and ACCESS1.0, EC-EARTH, HadGEM2-CC, HadGEM2-ES and CMCC-CM for CMIP5 scenarios. These models are therefore recommended for the estimation of future regional multi-model projections of surface variables driven by the atmospheric circulation in the north-east Atlantic Ocean region.     

Creating regional climate change scenarios over southern South America for the 2020’s and 2050’s using the pattern scaling technique: validity and limitations

Dynamical downscaling of global climate simulations is the most adequate tool to generate regional projections of climate change. This technique involves at least a present climate simulation and a simulation of a future scenario, usually at the end of the twenty first century. However, regional projections for a variety of scenarios and periods, the 2020s or the 2050s, are often required by the impact community. The pattern scaling technique is used to estimate information on climate change for periods and scenarios not simulated by the regional model. We based our study on regional simulations performed over southern South America for present climate conditions and two emission scenarios at the end of the twenty first century. We used the pattern scaling technique to estimate mean seasonal changes of temperature and precipitation for the 2020s and the 2050s. The validity of the scalability assumptions underlying the pattern scaling technique for estimating near future regional climate change scenarios over southern South America is assessed. The results show that the pattern scaling works well for estimating mean temperature changes for which the regional changes are linearly related to the global mean temperature changes. For precipitation changes, the validity of the scalability assumption is weaker. The errors of estimating precipitation changes are comparable to those inherent to the regional model and to the projected changes themselves.     

基于ConvLSTM机器学习的风暴潮漫滩预报研究

风暴潮是指由强烈的大气扰动所导致的海面异常升高现象,由热带气旋引起的风暴潮常对沿海地区造成巨大的社会经济、人类活动和生命财产危害。依靠数据驱动的强非线性映射能力的机器学习方法较传统数值模式预报在耗费研究资源和计算时间上更具优势。本文选取广东省珠江口为研究区域,基于卷积长短时记忆网络（Convolutional LSTM network,ConvLSTM）机器学习算法展开风暴潮漫滩预报研究,利用由再分析资料驱动的数值模式产品构建了历史台风漫滩数据集,用于机器学习模型训练、验证和测试。研究了两种预报方式,一种是基于海表面高度场的自回归预报,另一种是依赖预报风场和初始海表面高度场进行的预报;它们可以实现基于数据驱动的风暴潮漫滩预报,其中自回归预报模型表现更优。相较于传统动力学数值预报,基于数据驱动的ConvLSTM预报模型结构更为轻便,所需驱动数据更少,在缺少边界条件、地形、径流等信号时,在短临预报中仍能基本复现数值模式模拟的结果。     

Storm surge projections and implications for water management in South Florida

Water resource management in South Florida faces nearly intractable problems, in part due to weather and climate variability. Rising sea level and coastal storm surge are two phenomena with significant impacts on natural systems, fresh water supplies and flood drainage capability. However, decision support information regarding management of water resources in response to storm surge is not well developed. In an effort to address this need we analyze long term tidal records from Key West, Pensacola and Mayport Florida to extract surge distributions, to which we apply a nonlinear eustatic sea level rise model to project storm surge return levels and periods. Examination of climate connections reveals a statistically significant dependence between surge distributions and the Atlantic Multidecadal Oscillation (AMO). Based on a recent probabilistic model for AMO phase changes, we develop AMO-dependent surge distributions. These AMO-dependent surge projections are used to examine the flood control response of a coastal water management structure as an example of how climate dependent water resource forcings can be used in the formulation of decision support tools.     

Monitoring sea level changes

Future sea level rise arouses concern because of potentially deleterious impacts to coastal regions. These will stem not only from the loss of land through inundation and erosion, but also from increased frequency of storm floods, with a rising base level, even with no change in storm climatology, and from saltwater intrusion and greater amounts of waterlogging. Current sea level trends are important in formulating an accurate baseline for future projections. Sea level, furthermore, is an important parameter which integrates a number of oceanic and atmospheric processes. The ocean surface demonstrates considerable variability on diurnal, seasonal, and interannual time scales, induced by winds, storm waves, coastal upwelling, and geostrophic currents. Secular trends in sea level arise from changes in global mean temperature and also from crustal deformation on local to regional scales. The challenge facing researchers is how best to extract the climate signal from this noise.This paper re-examines recent estimates of sea level rise, discusses causes of variability in the sea level records, and describes methods employed to filter out some of these contaminating signals. Evidence for trends in long-term sea level records and in extreme events is investigated. Application of satellite geodesy to sea level research is briefly reviewed.     

Downscaling ability of one-way nested regional climate models: the Big-Brother Experiment

A methodology is developed for testing the downscaling ability of nested regional climate models (RCMs). The proposed methodology, nick-named the Big-Brother Experiment (BBE), is based on a "perfect-prognosis" approach and hence does not suffer from model errors nor from limitations in observed climatologies. The BBE consists in first establishing a reference climate by performing a large-domain high-resolution RCM simulation: this simulation is called the Big Brother. This reference simulation is then degraded by filtering short scales that are unresolved in today's global objective analyses (OA) and/or global climate models (GCMs) when integrated for climate projections. This filtered reference is then used to drive the same nested RCM (called the Little Brother), integrated at the same high-resolution as the Big Brother, but over a smaller domain that is embedded in the Big-Brother domain. The climate statistics of the Little Brother are then compared with those of the Big Brother over the Little-Brother domain. Differences can thus be attributed unambiguously to errors associated with the nesting and downscaling technique, and not to model errors nor to observation limitations. The results of the BBE applied to a one-winter-month simulation over eastern North America at 45-km grid-spacing resolution show that the one-way nesting strategy has skill in downscaling large-scale information to the regional scales. The time mean and variability of fine-scale features in a number of fields, such as sea level pressure, 975-hPa temperature and precipitation are successfully reproduced, particularly over regions where small-scale surface forcings are strong. Over other regions such as the ocean and away from the surface, the small-scale reproducibility is more difficult to achieve.     

Currents along the pacific coast of Canada

Abstract

A powerful storm passed over the coastal waters of eastern Canada on the 21 and 22 January 2000 causing significant damage to coastal infrastructure. The storm generated a large (>1.4 m) storm surge in the southern Gulf of St. Lawrence that unfortunately coincided with a high spring tide. This resulted in record high water levels in the southern Gulf of St. Lawrence (e.g., the highest level at Charlottetown since records began in 1911) and severe flooding around Prince Edward Island and along the eastern shore of New Brunswick.

During January 2000, a recently developed storm surge forecast system was running in pre‐operational mode at Dalhousie University. The core of the forecast system is a depth‐averaged, non‐linear, barotropic ocean model driven by forecast winds and air pressures produced by the Canadian Meteorological Centre's regional atmospheric forecast model. In this study we assess the forecast skill of the surge model for the 21 January storm by comparing its 24‐hour forecasts with two independent hourly dataseis: (i) sea levels recorded by 12 tide gauges located in eastern Canada and the north‐eastern United States, and (ii) depth‐mean currents recorded by an acoustic Doppler current profiler deployed on the outer Scotian Shelf. Overall, the forecasts of coastal sea level and depth‐mean currents are reasonable and have forecast errors below about 0.1 m and 0.1 m s^?1 respectively.     

Occurence Frequency of Storm Wind Waves in the Baltic,Black, and Caspian Seas under Changing Climate Conditions

The article proposes the method of climatic forecast of the occurrence frequency of synoptic conditions causing severe hydrometeorological events as well as severe events that are genetically related to them, in particular, storm wind waves. The choice of sea level pressure field as an indicator of atmospheric conditions of storm waves is substantiated. The algorithm for the method implementation is developed. It includes the processing of observational/reanalysis data; wind wave simulation; the systematization of synoptic conditions that accompany storm waves under the modern climate; the assessment of the ability of climate models of atmospheric and oceanic general circulation to simulate correctly the frequency of the revealed types of synoptic conditions for the modern climate; and the forecast of the frequency of these types for the possible scenarios of the future climate.     

Climate scenarios of sea level rise for the northeast Atlantic Ocean: a study including the effects of ocean dynamics and gravity changes induced by ice melt

Here we present a set of regional climate scenarios of sea level rise for the northeast Atlantic Ocean. In this study, the latest observations and results obtained with state-of-the-art climate models are combined. In addition, regional effects due to ocean dynamics and changes in the Earth’s gravity field induced by melting of land-based ice masses have been taken into account. The climate scenarios are constructed for the target years 2050 and 2100, for both a moderate and a large rise in global mean atmospheric temperature (2 °C and 4 °C in 2100 respectively). The climate scenarios contain contributions from changes in ocean density (global thermal expansion and local steric changes related to changing ocean dynamics) and changes in ocean mass (melting of mountain glaciers and ice caps, changes in the Greenland and Antarctic ice sheets, and (minor) terrestrial water-storage contributions). All major components depend on the global temperature rise achieved in the target periods considered. The resulting set of climate scenarios represents our best estimate of twenty-first century sea level rise in the northeast Atlantic Ocean, given the current understanding of the various contributions. For 2100, they yield a local rise of 30 to 55 cm and 40 to 80 cm for the moderate and large rise in global mean atmospheric temperature, respectively.     

Statistical Characteristics and Modeling of Storm Surges in the North Caspian Sea

The characteristics of storm surges obtained from sea level observations at four hydrometeorological stations in the North Caspian Sea for 2003–2017 are presented. The sea level that by 30 cm exceeds the monthly mean value at the analyzed point of the Caspian Sea was considered as a surge. In total, 370 surges were registered, 83% of them occurred during the cold season (September-April). The maximum surge height was 125 cm, the longest duration was 7 days. The most significant surges on Tyulenii Island were simulated with the operational hydrodynamic model of the sea level and currents of the Caspian Sea using atmospheric forcing from the COSMO model. The mean coefficient of correlation between the simulated and observed sea level is equal to 0.94.     

Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania

Summary Regional climate model and statistical downscaling procedures are used to generate winter precipitation changes over Romania for the period 2071–2100 (compared to 1961–1990), under the IPCC A2 and B2 emission scenarios. For this purpose, the ICTP regional climate model RegCM is nested within the Hadley Centre global atmospheric model HadAM3H. The statistical downscaling method is based on the use of canonical correlation analysis (CCA) to construct climate change scenarios for winter precipitation over Romania from two predictors, sea level pressure and specific humidity (either used individually or together). A technique to select the most skillful model separately for each station is proposed to optimise the statistical downscaling signal. Climate fields from the A2 and B2 scenario simulations with the HadAM3H and RegCM models are used as input to the statistical downscaling model. First, the capability of the climate models to reproduce the observed link between winter precipitation over Romania and atmospheric circulation at the European scale is analysed, showing that the RegCM is more accurate than HadAM3H in the simulation of Romanian precipitation variability and its connection with large-scale circulations. Both models overestimate winter precipitation in the eastern regions of Romania due to an overestimation of the intensity and frequency of cyclonic systems over Europe. Climate changes derived directly from the RegCM and HadAM3H show an increase of precipitation during the 2071–2100 period compared to 1961–1990, especially over northwest and northeast Romania. Similar climate change patterns are obtained through the statistical downscaling method when the technique of optimum model selected separately for each station is used. This adds confidence to the simulated climate change signal over this region. The uncertainty of results is higher for the eastern and southeastern regions of Romania due to the lower HadAM3H and RegCM performance in simulating winter precipitation variability there as well as the reduced skill of the statistical downscaling model.     

Storm Surges in the Strait of Georgia Simulated with a Regional Model

The Strait of Georgia is a large, semi-enclosed body of water between Vancouver Island and the mainland of British Columbia connected to the Pacific Ocean via Juan de Fuca Strait at the south and Johnstone Strait at the north. During the winter months, coastal communities along the Strait of Georgia are at risk of flooding caused by storm surges, a natural hazard that can occur when a strong storm coincides with high tide. This investigation produces storm surge hindcasts using a three-dimensional numerical ocean model for the Strait of Georgia and the surrounding bodies of water (Juan de Fuca Strait, Puget Sound, and Johnstone Strait) collectively known as the Salish Sea. The numerical model employs the Nucleus for European Modelling of the Ocean architecture in a regional configuration. The model is evaluated through comparisons of tidal elevation harmonics and storm surge with observations. Important forcing factors contributing to storm surges are assessed. It is shown that surges entering the domain from the Pacific Ocean make the most significant contribution to surge amplitude within the Strait of Georgia. Comparisons between simulations and high-resolution and low-resolution atmospheric forcing further emphasize that remote forcing is the dominant factor in surge amplitudes in this region. In addition, local wind patterns caused a slight increase in surge amplitude on the mainland side of the Strait of Georgia compared with Vancouver Island coastal areas during a major wind storm on 15 December 2006. Generally, surge amplitudes are found to be greater within the Strait of Georgia than in Juan de Fuca Strait.     

Projections of global warming-induced impacts on winter storm losses in the German private household sector

We present projections of winter storm-induced insured losses in the German residential building sector for the 21st century. With this aim, two structurally most independent downscaling methods and one hybrid downscaling method are applied to a 3-member ensemble of ECHAM5/MPI-OM1 A1B scenario simulations. One method uses dynamical downscaling of intense winter storm events in the global model, and a transfer function to relate regional wind speeds to losses. The second method is based on a reshuffling of present day weather situations and sequences taking into account the change of their frequencies according to the linear temperature trends of the global runs. The third method uses statistical-dynamical downscaling, considering frequency changes of the occurrence of storm-prone weather patterns, and translation into loss by using empirical statistical distributions. The A1B scenario ensemble was downscaled by all three methods until 2070, and by the (statistical-) dynamical methods until 2100. Furthermore, all methods assume a constant statistical relationship between meteorology and insured losses and no developments other than climate change, such as in constructions or claims management. The study utilizes data provided by the German Insurance Association encompassing 24 years and with district-scale resolution. Compared to 1971–2000, the downscaling methods indicate an increase of 10-year return values (i.e. loss ratios per return period) of 6–35 % for 2011–2040, of 20–30 % for 2041–2070, and of 40–55 % for 2071–2100, respectively. Convolving various sources of uncertainty in one confidence statement (data-, loss model-, storm realization-, and Pareto fit-uncertainty), the return-level confidence interval for a return period of 15 years expands by more than a factor of two. Finally, we suggest how practitioners can deal with alternative scenarios or possible natural excursions of observed losses.