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Wave parameters, such as wave height and wave period, are important for human activities, such as navigation, ocean engineering and sediment transport, etc. In this study, wave data from six buoys around Chinese waters, are used to assess the quality of wave height and wave period in the ERA5 reanalysis of the European Centre for Medium-Range Weather Forecasts. Annual hourly data with temporal resolution are used. The difference between the significant wave height (SWH) of ERA 5 and that of the buoy varies from −0.35 m to 0.30 m for the three shallow locations; for the three deep locations, the variation ranges from −0.09 m to 0.09 m. The ERA5 SWH data show positive biases, indicating an overall overestimation for all locations, except for E2 and S1 where underestimation is observed. During the tropical cyclone period, a large (about 32%) underestimation of the maximum SWH in the ERA5 data is observed. Hence, the ERA5 SWH data cannot be used for design applications without site-specific validation. The difference between the annual wave period from ERA5 and the mean wave period from the buoys varies from −1.31s to 0.4s. Inter-comparisons suggest that the ERA5 dataset is consistent with the annual mean SWH. However, for the average period, the performance is not good, and half of the correlation coefficients in the four points are less 50%. Overall, the deep water area simulation effect is better than that in the shallow water.

The tropical thermocline plays an important role in regulating equatorial sea surface temperature (SST); at present, it is still poorly simulated in the state-of-the-art climate models. In this paper, thermocline biases in the tropical North Pacific are investigated using the newly released CMIP6 historical simulations. It is found that CMIP6 models tend to produce an overly shallow thermocline in the northwestern tropics, accompanied by a deep thermocline in the northeastern tropics. A pronounced thermocline strength bias arises in the tropical northeastern Pacific, demonstrating a dipole structure with a sign change at about 8°N. These thermocline biases are accompanied with biases in the simulations of oceanic circulations, including a too weak North Equatorial Countercurrent (NECC), a reduction in water exchanges between the subtropics and the equatorial regions, and an eastward extension of the equatorward interior water transport. The causes of these thermocline biases are further analyzed. The thermocline bias is primarily caused by the model deficiency in simulating the surface wind stress curl, which can be further attributed to the longstanding double-ITCZ bias in the tropical North Pacific. Besides, thermocline strength bias can be partly attributed to the poor prescription of oceanic background diffusivity. By constraining the diffusivity to match observations, the thermocline strength in the tropical northeastern Pacific is greatly increased.

In the last decade, the Climate Limited-area Modeling (CLM) Community has contributed to the Coordinated Re- gional Climate Downscaling Experiment (CORDEX) with an extensive set of regional climate simulations. Using several versions of the COSMO-CLM community model, ERA-Interim reanalysis and eight Global Climate Models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were dynamically downscaled with horizontal grid spacings of 0.44° (∼50 km), 0.22° (∼25 km) and 0.11° (∼12 km) over the CORDEX domains Europe, South Asia, East Asia, Australasia and Africa. This major effort resulted in 80 regional climate simulations publicly available through the Earth System Grid Fed- eration (ESGF) web portals for use in impact studies and climate scenario assessments. Here we review the production of these simulations and assess their results in terms of mean near-surface temperature and precipitation to aid the future design of the COSMO-CLM model simulations. It is found that a domain-specific parameter tuning is beneficial, while increasing horizontal model resolution (from 50 to 25 or 12 km grid spacing) alone does not always improve the performance of the simulation. Moreover, the COSMO-CLM performance depends on the driving data. This is generally more important than the dependence on horizontal resolution, model version and configuration. Our results emphasize the importance of performing regional climate projections in a coordinated way, where guidance from both the global (GCM) and regional (RCM) climate modelling communities is needed to increase the reliability of the GCM-RCM modelling chain.

Sea surface temperature (SST) bias in the climate models has been a focus in the past, but subsurface temperature biases have not been received much attention yet. In this study, subsurface temperature biases in the Tropical North Pacific (TNP) are investigated by analyzing the CMIP6, CMIP5 and OMIP products, and performing ocean model simulations. It is found that almost all the CMIP and OMIP simulations have a pronounced subsurface warm bias (SWB) in the northeastern tropical Pacific (NETP), and the model developments over the past decade do not indicate obvious improvements in bias pattern and magnitude from CMIP5 to the latest version CMIP6. This SWB is primarily caused by the model deficiencies in the simulated surface wind stress curl (WSC) in the NETP, which is too weak to produce a sufficient Ekman upwelling, a bias that also exists in OMIP simulations. The uncertainties in the parameterizations of the oceanic vertical mixing processes also make a great contribution, and it is demonstrated that the estimated oceanic vertical diffusivities are overestimated both in the upper boundary layer and the interior in the CMIP and OMIP simulations. The relationship between the SWB and the misrepresented oceanic vertical mixing processes are investigated by conducting several ocean-only experiments, in which the upper boundary layer mixing is modified by reducing the wind stirring effect in the Kraus-Turner type bulk mixed-layer approach, and the interior mixing is constrained by using the Argo-derived diffusivity. By applying these modifications to oceanic vertical mixing schemes, the SWB is greatly reduced in the NETP. The consequences of this SWB are further analyzed. Because the thermal structure in the NETP can influence the simulations of oceanic circulations and equatorial upper-ocean thermal structure, the large SWB in the CMIP6 models tends to produce a weak equatorward water transport in the subsurface TNP, a weak equatorial upwelling and a warm equatorial upper ocean.

Anthropogenic forcings have reduced the likelihood of heavy precipitation in southern China like the 2019 March-July event by about 60%

Global warming is likely to cause a progressive drought increase in some regions, but how population and natural resources will be affected is still underexplored. This study focuses on global population, forests, croplands and pastures exposure to meteorological drought hazard in the 21st century, expressed as frequency and severity of drought events. As input, we use a large ensemble of climate simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX), population projections from the NASA-SEDAC dataset and land-use projections from the Land-Use Harmonization 2 project for 1981–2100. The exposure to drought hazard is presented for five Shared Socioeconomic Pathways (SSP1-SSP5) at four Global Warming Levels (GWLs: 1.5°C to 4°C). Results show that considering only Standardized Precipitation Index (SPI; based on precipitation), the SSP3 at GWL4 projects the largest fraction of the global population (14%) to experience an increase in drought frequency and severity (versus 1981–2010), with this value increasing to 60% if temperature is considered (indirectly included in the Standardized Precipitation-Evapotranspiration Index, SPEI). With SPEI, considering the highest GWL for each SSP, 8 (for SSP2, SSP4, SSP5) and 11 (SSP3) billion people, that is, more than 90%, will be affected by at least one unprecedented drought. For SSP5 at GWL4, approximately 2 × 10$^{6}$ km$^{2}$ of forests and croplands (respectively, 6% and 11%) and 1.5 × 10$^{6}$ km$^{2}$ of pastures (19%) will be exposed to increased drought frequency and severity according to SPI, but for SPEI this extent will rise to 17 × 10$^{6}$ km$^{2}$ of forests (49%), 6 × 10$^{6}$ km$^{2}$ of pastures (78%) and 12 × 10$^{6}$ km$^{2}$ of croplands (67%), being mid-latitudes the most affected. The projected likely increase of drought frequency and severity significantly increases population and land-use exposure to drought, even at low GWLs, thus extensive mitigation and adaptation efforts are needed to avoid the most severe impacts of climate change.

The ability of forecasting systems to simulate tropical cyclones is still insufficient, and currently, there is an increased interest in improving model performance for intense tropical cyclones. In this study, the impact of reducing surface drag at high wind speeds on modeling wind and wave conditions during the super Typhoon Lingling event over the northwest Pacific Ocean in 2019 is investigated. The model response with respect to the parameterization for momentum exchange at the ocean surface is demonstrated using a fully coupled regional atmosphere model (the Consortium for Small-Scale Modeling-Climate Limited-area Modeling, CCLM) and a wind wave model (WAM). The active two-way coupling between the atmosphere and ocean waves model is enabled through the introduction of sea state-dependent surface drag into the CCLM and updated winds into the WAM. The momentum exchange with the sea surface is modeled via the dependency of the roughness length (Z0) on the surface stress itself and, when applicable, on the wind speed. Several high-resolution runs are performed using one-way or two-way fully coupled regional atmosphere-wave (CCLM-WAM) models. The model simulations are assessed against the best track data as well as against buoy and satellite observations. The results show that the spectral nudging technique can improve the model’s ability to capture the large-scale circulation, track and intensity of Typhoon Lingling at regional scales. Under the precondition of large-scale constraining, the two-way coupling simulation with the proposed new roughness parameterization performs much better than the simulations used in older studies in capturing the maximum wind speed of Typhoon Lingling due to the reduced drag at extreme wind conditions for the new Z0.

Understanding the driving factors for precipitation extremes matters for adaptation and mitigation measures against the changing hydrometeorological hazards in Yangtze River basin, a habitable area that provides water resources for domestic, farming, and industrial needs. However, the region is naturally subject to major floods linked to monsoonal heavy precipitation during May–September. This study aims to quantify anthropogenic influences on the changing risk of 2-week-long precipitation extremes such as the July 2019 extreme cases, as well as events of shorter durations, over the middle and lower reaches of Yangtze River basin (MLYRB). Precipitation extremes with different durations ranging from 1-day to 14-days maximum precipitation accumulations are investigated. Gridded daily precipitations based on nearly 2,400 meteorological stations across China are used to define maximum accumulated precipitation extremes over the MLYRB in July during 1961–2019. Attribution analysis is conducted by using the Met Office HadGEM3-GA6 modeling system, which comprises two sets of 525-member ensembles for 2019. One is forced with observed sea-surface temperatures (SSTs), sea-ice and all forcings, and the other is forced with preindustrialized SSTs and natural forcings only. The risk ratio between the exceedance probabilities estimated from all-forcing and natural-forcing simulations is calculated to quantify the anthropogenic contribution to the changing risks of the July 2019–like precipitation extremes. The results reveal that anthropogenic warming has reduced the likelihood of 2019-like 14-days heavy precipitation over the mid–lower reaches of the Yangtze River by 20%, but increased that of 2-days extremes by 30%.

In this study, we investigate the skills of the regional climate model Consortium for Small-Scale Modeling in Climate Mode (CCLM) in reproducing historical climatic features and their added value to the driving global climate models (GCMs) of the Coordinated Regional Climate Downscaling Experiment—East Asia (CORDEX-EA) domain. An ensemble of climate simulations, with a resolution of 0.44°, was conducted by downscaling four GCMs: CNRM-CM5, EC-EARTH, HadGEM2, and MPI-ESM-LR. The CCLM outputs were compared with different observations and reanalysis datasets. Results showed strong seasonal variability of CCLM’s ability in reproducing climatological means, variability, and extremes. The bias of the simulated summer temperatures is generally smaller than that of the winter temperatures; in addition, areas where CCLM adds value to the driving GCMs in simulating temperature are larger in the winter than in the summer. CCLM outperforms GCMs in terms of generating climatological precipitation means and daily precipitation distributions for most regions in the winter, but this is not always the case for the summer. It was found that CCLM biases are partly inherited from GCMs and are significantly shaped by structural biases of CCLM. Furthermore, downscaled simulations show added value in capturing features of consecutive wet days for the tropics and of consecutive dry days for areas to the north of 30°N. We found considerable uncertainty from reanalysis and observation datasets in temperatures and precipitation climatological means for some regions that rival bias values of GCMs and CCLM simulations. We recommend carefully selecting reference datasets when evaluating modeled climate means.

Tide–surge interaction plays an important role in the distribution of surges along the coast of China. A comprehensive understanding of tide–surge interaction will provide more accurate estimates of extreme sea levels and storm surges. This study applied a statistical method to hourly tide–surge data from 12 tide gauges located along the coast of China to examine the dependency of residual maxima on tidal phases. Statistical significance has been tested quantitatively for each site utilizing the chi-square test. Results show that significant tide–surge interaction exists at all 12 tide gauge sites, but the strength of the interactions differs according to location. The distributions of peak residuals are significantly different from the uniform distribution and can be divided into three types. In the first type, the residual generally reaches its peak value during the rising tide (2–4 h before high tide), whereas in the second type, the residual reaches its peak mostly during the ebb tide (2–4 h after the high tide). In the third type, the residual reaches peaks during both ebb and rising tides. The main causes of tide–surge interactions are the tidal phase alteration caused by surge and the modulation of surge due to tides at the tide gauge. Tide–surge interactions at Lianyungang and Xiamen are more significant than those at other gauge sites. At Kanmen and Keelung, the tide–surge interactions are weakest. Variations of the tide–surge interaction at Xiamen and Quarry Bay show that the interaction has not varied significantly during the past 40 years and that tide–surge interactions tend to be more stable with the strengthening of the interaction.

An overview of Sun–Sky Radiometer Observation Network (SONET) measurements in China is presented. Based on observations at 16 distributed SONET sites in China, atmospheric aerosol parameters are acquired via standardization processes of operational measurement, maintenance, calibration, inversion, and quality control implemented since 2010. A climatology study is performed focusing on total columnar atmospheric aerosol characteristics, including optical (aerosol optical depth, ÅngstrÖm exponent, fine-mode fraction, single-scattering albedo), physical (volume particle size distribution), chemical composition (black carbon; brown carbon; fine-mode scattering component, coarse-mode component; and aerosol water), and radiative properties (aerosol radiative forcing and efficiency). Data analyses show that aerosol optical depth is low in the west but high in the east of China. Aerosol composition also shows significant spatial and temporal variations, leading to noticeable diversities in optical and physical property patterns. In west and north China, aerosols are generally affected by dust particles, while monsoon climate and human activities impose remarkable influences on aerosols in east and south China. Aerosols in China exhibit strong light-scattering capability and result in significant radiative cooling effects.

This paper demonstrates two important aspects of regional dynamical downscaling of multidecadal atmospheric reanalysis. First, that in this way skillful regional descriptions of multidecadal climate variability may be constructed in regions with little or no local data. Second, that the concept of large-scale constraining allows global downscaling, so that global reanalyses may be completed by additions of consistent detail in all regions of the world. Global reanalyses suffer from inhomogeneities. However, their large-scale componenst are mostly homogeneous; Therefore, the concept of downscaling may be applied to homogeneously complement the large-scale state of the reanalyses with regional detail-wherever the condition of homogeneity of the description of large scales is fulfilled. Technically, this can be done by dynamical downscaling using a regional or global climate model, which's large scales are constrained by spectral nudging. This approach has been developed and tested for the region of Europe, and a skillful representation of regional weather risks-in particular marine risks-was identified. We have run this system in regions with reduced or absent local data coverage, such as Central Siberia, the Bohai and Yellow Sea, Southwestern Africa, and the South Atlantic. Also, a global simulation was computed, which adds regional features to prescribed global dynamics. Our cases demonstrate that spatially detailed reconstructions of the climate state and its change in the recent three to six decades add useful supplementary information to existing observational data for midlatitude and subtropical regions of the world.

Two questions motivated this study: 1) Will meteorological droughts become more frequent and severe during the twenty-first century? 2) Given the projected global temperature rise, to what extent does the inclusion of temperature (in addition to precipitation) in drought indicators play a role in future meteorological droughts? To answer, we analyzed the changes in drought frequency, severity, and historically undocumented extreme droughts over 1981–2100, using the standardized precipitation index (SPI; including precipitation only) and standardized precipitation-evapotranspiration index (SPEI; indirectly including temperature), and under two representative concentration pathways (RCP4.5 and RCP8.5). As input data, we employed 103 high-resolution (0.448) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX), based on a combination of 16 global circulation models (GCMs) and 20 regional circulation models (RCMs). This is the first study on global drought projections including RCMs based on such a large ensemble of RCMs. Based on precipitation only,;15% of the global land is likely to experience more frequent and severe droughts during 2071–2100 versus 1981–2010 for both scenarios. This increase is larger (;47% under RCP4.5,;49% under RCP8.5) when precipitation and temperature are used. Both SPI and SPEI project more frequent and severe droughts, especially under RCP8.5, over southern South America, the Mediterranean region, southern Africa, southeastern China, Japan, and southern Australia. A decrease in drought is projected for high latitudes in Northern Hemisphere and Southeast Asia. If temperature is included, drought characteristics are projected to increase over North America, Amazonia, central Europe and Asia, the Horn of Africa, India, and central Australia; if only precipitation is considered, they are found to decrease over those areas. Fil: Spinoni, Jonathan. Joint Research Centre; Italia Fil: Barbosa, Paulo. Joint Research Centre; Italia Fil: Bucchignani, Edoardo. Centro Euro Mediterraneo Sui Cambiamenti Climatici.; Italia Fil: Cassano, John. State University of Colorado at Boulder; Estados Unidos Fil: Cavazos, Tereza. Centro de Investigacion Cientifica y de Educacion Super; México Fil: Christensen, Jens H.. Danish Meteorological Institute; Dinamarca Fil: Christensen, Ole B.. Danish Meteorological Institute; Dinamarca Fil: Coppola, Erika. The Abdus Salam. International Centre for Theoretical Physics; Italia Fil: Evans, Jason. University of New South Wales. Faculty of Science; Australia Fil: Geyer, Beate. Institute of Coastal Research; Alemania Fil: Giorgi, Filippo. The Abdus Salam. International Centre for Theoretical Physics; Italia Fil: Hadjinicolaou, Panos. Environment And Water Research Center; Chipre Fil: Jacob, Daniela. Climate Service Center; Alemania Fil: Katzfey, Jack. Commonwealth Scientific And Industrial Research Organis; Australia Fil: Koenigk, Torben. Swedish Meteorological And Hydrological; Suecia Fil: Laprise, René. Université du Québec a Montreal; Canadá Fil: Lennard, Christopher J.. University of Cape Town; Sudáfrica Fil: Kurnaz, M. Levent. Bogazici University; Turquía Fil: Delei, L.I.. Chinese Academy of Sciences; República de China Fil: Llopart, Marta. Universidade Estadual Paulista Julio de Mesquita Filho; Brasil Fil: McCormick, Niall. European Commission Joint Research Centre; España Fil: Naumann, Gustavo. European Commission Joint Research Centre; España Fil: Nikulin, Grigory. No especifíca; Fil: Ozturk, Tugba. No especifíca; Fil: Panitz, Hans Juergen. No especifíca; Fil: da Rocha, Rosmeri Porfirio. No especifíca; Fil: Rockel, Burkhardt. No especifíca; Fil: Solman, Silvina Alicia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina Fil: Syktus, Jozef. The University of Queensland; Australia. University of Queensland; Australia Fil: Tangang, Fredolin. No especifíca; Fil: Vautard, Robert. No especifíca; Fil: Vautard, Robert. Centre National de la Recherche Scientifique; Francia Fil: Vogt, Jürgen V.. European Commission Joint Research Centre; España Fil: Winger, Katja. Université du Québec a Montreal; Canadá Fil: Zittis, George. No especifíca; Fil: Dosio, Alessandro. No especifíca

A better understanding of the storm surge phenomenon is essential for coastal protection, future planning, and coastal ecosystem conservation. Using hourly sea level data from 15 tide gauges along the coastline of China the metrics, skew surge and residual water level, which are used to represent storm surges, are compared in this study. The relationship between residual water level, skew surge and predicted high tide were analyzed using two statistical measurements. The results showed that unlike the annual maximum residual water level, the annual maximum skew surge and the astronomical high tide tended to coincide more frequently (more than 50% of the annual maximum skew surges occurred at high tide). Thus, the skew surge was more suitable than the residual water level for use in risk-based coastal planning frameworks. Using the monthly maximum value and indices of the storm surge (storm surge number, duration and intensity), we compared the performances of skew surge and residual water level in expressing changes of storm surges. These two metrics can both capture changes in storm surge activities on seasonal, decadal and long-term scales, and correlation coefficients of storm surge intensity between residual water level and skew surge were greater than 0.80 at 11 tide gauges. At most tide gauges, the skew surge was significantly correlated with 4–5 nearby tide gauges while the residual water level was correlated with only 1–2 tide gauges, the skew surge may be a better metric for risk-based coastal planning frameworks if the interpolation method is used. The El Nino-Southern Oscillation (ENSO) can affect the storm surge frequency along the coastline of China. The effects of the ENSO were more obvious when the residual water level was selected. In conclusion, the skew surge can be selected for use in risk-based coastal planning frameworks, while the residual water level may be a better metric for capturing the relationship between storm surges and climate changes.

China has set ambitious goals for the development of offshore wind energy to meet the increasing energy demand of coastal provinces. Many studies have assessed the potential offshore wind energy in Chinese territorial waters. However, few studies have focused on the climatology, variability, and extreme climate of wind speeds and wind power, especially at hub height in this area. This type of study is important for selecting promising sites for offshore wind farms. In the present study, a 35-year (1979–2013) high-resolution (7 km) wind hindcast over the Bohai Sea and the Yellow Sea (BYS) at 100-m height was constructed using the regional climate model COSMO-CLM (CCLM) driven by the ERA-Interim reanalysis dataset. The quality of wind speeds reconstructed by CCLM was assessed by a comparison with observation data at several stations. After verification, the climatology, variability, and extreme climate of winds over the BYS were spatially and temporally investigated. The results show that the 35-year mean wind speed is mostly between 7.0 and 7.5 m/s; in the coastal areas of the BYS, the mean is less than 7.0 m/s, and in the remote offshore areas, the mean is greater than 7.5 m/s. The daily mean wind speed is stronger (weaker) in winter (summer) half year, with stronger (weaker) spatial variability. Wind power density is mainly 300–500 W/m2. The interannual variability of annual mean wind speed and the wind power are in the range of 0.1–0.3 m/s and 10–40 W/m2, respectively. Decadal variances of the mean wind speed and the wind power are roughly within ±2% and ±5%, respectively, with a stronger variability along the southwestern coasts of the Yellow Sea. The distribution patterns of extreme winds (i.e., 5, 10, 30, and 50-year return values) are generally similar, with strength increasing from the northwest to the southeast. The wind energy characteristics for water areas and potential wind farm sites are summarized.

Added value (AV) from dynamical downscaling has long been a crucial and debatable issue in regional climate studies. To assess AV generated by COSMO Climate Local Model (CCLM), a model-reconstructed hindcast with 7 km grid resolution was compared against the forcing data set ERA-Interim (ERA-I) over the Bohai Sea and the Yellow Sea, with satellite and in situ observation as reference. Both quantitative metrics and qualitative assessments have been used in the investigation of AV by CCLM. Land surface winds, extreme winds, a typhoon, a cold surge and a vortex street have been selected in the assessment process. Statistical analysis on surface winds reveals that high-resolution CCLM hindcast can add value to ERA-I in reproducing wind intensities and direction, probability distribution and extreme winds mainly at mountain areas, which may be related to the highly resolved and accurate description of terrain roughness length and orographic barriers in CCLM. With respect to atmospheric processes, CCLM outperforms ERA-I in resolving detailed temporal and spatial structures for phenomena of a typhoon and of a cold surge; CCLM generates some orography-related phenomena such as a vortex street, which is not captured by ERA-I. These AVs demonstrate the utility of the 7-km resolution CCLM for regional and local climate studies and applications.

This study performed several sensitivity experiments to investigate the impact of atmosphere&ndash, wave coupling on the simulated wind and waves over the East China Sea (ECS) with a focus on typhoon events. These experiments include stand-alone regional atmosphere model (CCLM) simulations, stand-alone spectral wave model (WAM) simulations driven by the regional atmospheric model CCLM or ERA5 reanalysis, and two-way (CCLM-WAM) coupled simulations. We assessed the simulated wind speed and significant wave height against in situ observations and remote sensing data and focused on typhoon events in 2010. We analyzed the differences between the experiments in capturing the surface pressure, wind speed, and roughness length. Both ERA5 reanalysis data and our regional model simulations demonstrate high quality in capturing wind and wave conditions over the ECS. The results show that downscaled simulations tend to be closer to in situ observations than ERA5 reanalysis data in capturing wind variability and probability distribution, dominant wind and wave directions, strong typhoon intensity and related extreme significant wave height. In comparison with satellite observations, the CCLM-WAM simulation outperforms the CCLM in reducing wind bias. The coupled and uncoupled simulations are very similar in terms of other wind and wave statistics. Though there is much improvement in capturing typhoon intensity to ERA5, regional downscaled simulations still underestimate the wind intensity of tropical cyclones.

Using hourly sea level data from 15 tide gauges along the Chinese coast and sea level data of three simulations of the Coupled Model Intercomparison Project Phase 5 (CMIP5), we assessed the changes and benefits of the extreme sea level of limiting warming to 1.5°C instead of 2.0°C. Observations show that the extreme sea level has risen with high confidence during the past decades along the coast of China, while the mean sea level change, especially the long-term change plays important roles in the changing process of extreme sea levels. Under the 1.5°C and 2.0°C warming scenarios, the sea level will rise with fluctuations in the future, so will the return levels of the extreme sea levels. Compared with the 1.5°C warming condition, the return levels under the 2.0°C warming condition will rise significantly at all tide gauges along the Chinese coast. The results indicate that a 0.5°C warming will bring much difference to the extreme sea levels along the coast of China. It is of great necessity to limit anthropogenic warming to 1.5°C rather than 2.0°C, as proposed by the Paris Climate Agreement, which will greatly reduce the potential risks of future flood disasters along the coast of China and is beneficial for risk response management.

We review the state of dynamical downscaling with scale-constrained regional and global models. The methodology, in particular spectral nudging, has become a routine and well-researched tool for hindcasting climatologies of sub-synoptic atmospheric disturbances in coastal regions. At present, the spectrum of applications is expanding to other phenomena, but also to ocean dynamics and to extended forecasting. Additionally, new diagnostic challenges are appearing such as spatial characteristics of small-scale phenomena such as Low Level Jets.

We review the state of dynamical downscaling with scale-constrained regional and global models. The methodology, in particular spectral nudging, has become a routine and well-researched tool for hindcasting climatologies of sub-synoptic atmospheric disturbances in coastal regions. At present, the spectrum of applications is expanding to other phenomena, but also to ocean dynamics and to extended forecasting. Also new diagnostic challenges are appearing such as spatial characteristics of small-scale phenomena such as Low Level Jets.

The present study mainly investigates the storm surge variations at different temporal scales using hourly tide gauge data in the Bohai Sea. The seasonal variation, inter-decadal variation, long-term trend and the tide-surge interaction were analyzed separately. The results show that the storm surges in the southwest Bohai Sea are larger than those in the north. The storm surges were more serious in winter (Oct. to Mar.) than in summer half of the year. Significant inter-decadal variations exist in the Bohai Sea, and the extreme storm surge events have been intensifying since 2010. Storm surge intensities at three of the tide gauges (Qinhuangdao, Huludao and Tanggu) exhibited a decreasing trend from 1980 to 2016, with trends significant at the 95% level at Qinhuangdao and Tanggu. Significant tide-surge interactions were observed at all four tide gauges. The tide-surge interaction that results in peak surges mostly occurs during the flood and ebb tides. There is a statistically significant negative correlation between storm surge intensity and the Arctic Oscillation (AO) at Longkou and Tanggu, while there is a significantly positive correlation between storm surge intensity and the Siberian High (SH) at Huludao, Qinhuangdao and Tanggu. A linear regression analysis revealed that the variations of the AO and SH explained 19–48% of the variations in the storm surge intensity in the Bohai Sea.

A 34 year (1979–2012) high-resolution (7 km grid) atmospheric hindcast over the Bohai Sea and the Yellow Sea (BYS) has been performed using COSMO-CLM (CCLM) forced by ERA-Interim reanalysis data (ERA-I). The accuracy of CCLM in surface wind reproduction and the added value of dynamical downscaling to ERA-I have been investigated through comparisons with the QuikSCAT Level2B 12.5 km version 3 (L2B12v3) swath data and in situ observations. The results revealed that CCLM has a reliable ability to reproduce the regional wind characteristics over the BYS. Added value to ERA-I has been detected in the coastal areas with complex orography. CCLM wind quality had strong seasonal variability, with better performance in the summer relative to ERA-I, even in the offshore areas. CCLM was better able to represent light and moderate winds but had even more added value for strong winds relative to ERA-I. The spatial digital filter method was used to investigate the scale of the added value, and the results show that CCLM adds value to ERA-I mainly in medium scales of wind variability. Furthermore, wind climatology was investigated, and significant increasing trends in the south Yellow Sea especially in winter and spring were found for seasonal mean wind speeds.

Reanalysis data sets have been widely used in regional climate dynamical downscaling studies. In this study, we test the use of various reanalysis data sets in constraining dynamical downscaling by assessing the reconstruction skill of the Yellow Sea coastal winds using the COSMO model in Climate Mode (CCLM) with 7-km resolution. Four reanalysis forcing data sets are used as lateral boundary conditions and internal large-scale constraints (spectral nudging): the National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set (NCEP1) is downscaled to an intermediate domain with 55-km resolution (CCLM_55km), ERA-interim reanalysis data set (ERAint), NCEP climate forecast system reanalysis data set (CFSR), and Japanese 55-year reanalysis data set (JRA55). Several statistical analysis methods are employed to assess the modeled winds through comparison with observed offshore wind data from 2006, and it is found that the downscaled simulations yield good quality wind speed products. However, they all tend to overestimate observed low wind speeds and underestimate observed high wind speeds. Furthermore, the quality of the modeled wind direction is strongly associated with the wind speed intensities, exhibiting a much better reproduction of wind direction at strong wind speeds than at light wind speeds. The downscaling simulations driven by ERAint, JRA55, and CFSR are consistent with each other in the reproduction of local wind speed and direction; the simulations driven by ERAint and JRA55 are slightly better for strong winds and those driven by CFSR are better for light winds. All three simulations generate local wind estimates that are superior to those of the simulation driven by CCLM_55km. This superiority reflects the better quality of the CFSR, ERAint, and JRA55 reanalyses with regard to assimilated local observations compared with the CCLM_55km hindcast, which exploits only upper-air large scale NCEP1 wind fields.

Using hourly sea level data from four tide gauges, the changes of the extreme sea level in the Bohai Sea were analyzed in this work. Three components (i.e., mean sea level, tide and surge) as well as the tide&ndash, surge interaction were studied to find which component was important in the changes of extreme sea levels. Significant increasing trends exist in the mean sea level at four tide gauges from 1980 to 2016, and the increase rate ranges from 0.2 to 0.5 cm/year. The mean high tide levels show positive trends at four tide gauges, and the increasing rate (0.1 to 0.3 cm/year) is not small compared with the long-term trends of the mean sea levels. However, the mean tidal ranges show negative trends at Longkou, Qinhuangdao and Tanggu, with the rate from about &minus, 0.7 to &minus, 0.2 cm/year. At Qinhuangdao and Tanggu, the annual surge intensity shows explicit long-term decreasing trend. At all four tide gauges, the storm surge intensity shows distinct inter-annual variability and decadal variability. All four tide gauges show significant tide&ndash, surge interaction, the characteristics of the tide&ndash, surge interaction differ due to their locations, and no clear long-term change was found. Convincing evidence implies that the extreme sea levels increase during the past decades from 1980 to 2016 at all tide gauges, with the increasing rate differing at different percentile levels. The extreme sea level changes in the Bohai Sea are highly affected by the changes of mean sea level and high tide level, especially the latter. The surge variation contributes to the changes of extreme sea level at locations where the tide&ndash, surge interaction is relatively weak.