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Our world is rapidly changing. Societies are facing an increase in the frequency and intensity of high-impact and extreme weather and climate events. These extremes together with exponential population growth and demographic shifts (e.g., urbanization, increase in coastal populations) are increasing the detrimental societal and economic impact of hazardous weather and climate events. Urbanization and our changing global economy have also increased the need for accurate projections of climate change and improved predictions of disruptive and potentially beneficial weather events on kilometer scales. Technological innovations are also leading to an evolving and growing role of the private sector in the weather and climate enterprise. This article discusses the challenges faced in accelerating advances in weather and climate forecasting and proposes a vision for key actions needed across the private, public, and academic sectors. Actions span (i) utilizing the new observational and computing ecosystems; (ii) strategies to advance Earth system models; (iii) ways to benefit from the growing role of artificial intelligence; (iv) practices to improve the communication of forecast information and decision support in our age of internet and social media; and (v) addressing the need to reduce the relatively large, detrimental impacts of weather and climate on all nations and especially on low-income nations. These actions will be based on a model of improved cooperation between the public, private, and academic sectors. This article represents a concise summary of the white paper on the Future of Weather and Climate Forecasting (2021) put together by the World Meteorological Organizations’ Open Consultative Platform.

In operational flood forecast systems, the effect of sea ice is typically neglected or parameterized solely in terms of ice concentration. In this study, an efficient way of adding ice effects to the global total water level prediction systems, via the ice–ocean stress, is described and evaluated. The approach features a novel, consistent representation of the tidal relative ice–ocean velocities, based on a transfer function derived from ice and ocean tidal ellipses given by an external ice–ocean model. The approach and its impact are demonstrated over four ice seasons in the Northern Hemisphere, using in situ observations and model predictions. We show that adding ice effects helps the model reproduce most of the observed seasonal modulations in tides (up to 40 % in amplitude and 50∘ in phase for M2) in the Arctic and Hudson Bay. The dominant driving mechanism for the seasonal modulations is shown to be the under-ice friction, acting in areas of shallow water (less than 100 m) and its accompanied large shifts in the amphidromes (up to 125 km). Important contributions from baroclinicity and tide–surge interaction due to ice–ocean stress are also found in the Arctic. Both mechanisms generally reinforce the seasonal modulations induced by the under-ice friction. In forecast systems that neglect or rely on simple ice concentration parameterizations, storm surges tend to be overestimated. With the inclusion of ice–ocean stress, surfaces stresses are significantly reduced (up to 100 % in landfast ice areas). Over the four ice seasons covered by this study, corrections up to 1.0 m to the overestimation of surges are achieved. Remaining limitations regarding the overestimated amphidrome shifts and insufficient ice break-up during large storms are discussed. Finally, the anticipated trend of increasing risk of coastal flooding in the Arctic, associated with decreasing ice and its profound impact on tides and storm surges, is briefly discussed.

The water following characteristics of six different drifter types are investigated using two different operational marine environmental prediction systems: one produced by Environment and Climate Change Canada (ECCC) and the other produced by MET Norway (METNO). These marine prediction systems include ocean circulation models, atmospheric models, and surface wave models. Two leeway models are tested for use in drift object prediction: an implicit leeway model where the Stokes drift is implicit in the leeway coefficient, and an explicit leeway model where the Stokes drift is provided by the wave model. Both leeway coefficients are allowed to vary in direction and time in order to perfectly reproduce the observed drifter trajectory. This creates a time series of the leeway coefficients that exactly reproduce the observed drifter trajectories. Mean values for the leeway coefficients are consistent with previous studies that utilized direct observations of the leeway. For all drifters and models, the largest source of variance in the leeway coefficient occurs at the inertial frequency and the evidence suggests it is related to uncertainties in the ocean inertial currents.

In the context of Canada’s Ocean Protection Plan (OPP), improved coastal and near-shore modelling is needed to enhance marine safety and emergency response capacity in the aquatic environment. In this study, the Nucleus for European Modelling of the Ocean (NEMO) is adopted to develop an ocean forecasting system for Saint John harbour in the Bay of Fundy, on the east coast of Canada. The challenging regional oceanography is characterized by the presence of some of the world’s strongest tides, significant river runoff and complicated geometry. A three-level one-way nesting approach is used to downscale from a 1/12° North Atlantic-Arctic regional model to very-high-resolution port-scale around Saint John harbour. The three nested grids cover the outer shelf, the Bay of Fundy and finally the approach to the harbour with resolutions of 2.5 km, 500 m and 100 m respectively. Due to the lack of accurate runoff data at the Saint John River outlet, the model’s lateral open boundary condition is modified to introduce the river forcing with the observed time series of water level near the mouth of the river. Evaluation with observational data demonstrates the model’s accuracy for the simulation of tidal elevation and currents, non-tidal water level and currents, temperature and salinity. Comparison with the observed sea surface temperature demonstrates the improved model accuracy through increasing the horizontal resolution. Virtual Lagrangian trajectories computed using the modelled surface currents and including wind effects show good agreement with the observed trajectories of different types of surface drifters. This study demonstrates the capability of the NEMO modelling framework to provide very-high-resolution modelling at port-scale resolution for the Saint John harbour.

Accurate forecasts of storm surges caused by winds and atmospheric pressure are important for the protection of life and property in coastal regions and also for safe navigation. Therefore, Environment and Climate Change Canada (ECCC) maintains and develops surge prediction systems. This study focuses on the assessment of the timing and amplitude of predicted surges at selected locations during the passage of hurricane Dorian. The systems use barotropic ocean models to simulate water levels and currents at 1/30 and 1/12 degree horizontal resolutions.The relatively low tidal range at the time of Dorian’s landfall helped prevent catastrophic flooding. However, with a closer superposition of the peak surge and high tide, the damage could have been more significant. Reasonably well predicted timing of the highest surge by the system helped prevent the over-issuance of warnings. Sensitivity of the forecast quality to the lead time, resolution and atmospheric forcing for the event will be presented.

Concern over increased flooding and the need for earlier and more reliable risk forecasts motivate the continued development of operational forecasts of coastal water level. We report here on results from a year long ensemble of total water level forecasts calculated using a dynamical ocean model forced with ensemble atmospheric forcing and tidal boundary conditions. We focus on the east coast of Canada. The domain includes the Gulf of St. Lawrence, the Labrador Shelf, the Scotian Shelf, and the Gulf of Maine. The water level ensemble is made of a control and 20 perturbed members. Individual forecasts are produced twice daily for 16 days. The novelty of the present study is in the exploration of perturbations of the ocean contributions. In addition to examining how uncertainty in atmospheric forcing maps into flood risk, we also explore the feasibility, and impact, of perturbing the ocean tides. We use a recent case study to demonstrate our findings.

The development of a computationally efficient scheme for predicting the global distribution of total water level (TWL) is discussed. The ocean model is barotropic, has a horizontal grid spacing of 1/12°, and is based on the NEMO modeling framework. It is forced by the gravitational potential and hourly atmospheric fields for 2008. Hourly time spacing was required to resolve the S 2 tide in global air pressure and wind. The predicted tide in water deeper than 400 m was nudged to TPXO8 “observations” of tidal elevation or current using a scheme called tidal nudging (Kodaira et al., 2019). The benefit of nudging horizontal velocity in the momentum equation, compared to sea level in the continuity equation, is discussed. Tidal nudging is shown to improve tidal predictions of sea level at the coast, particularly at the S 2 tidal frequency. The predicted radiational S 2 tide in sea level forced solely by the S 2 tide in global air pressure reaches amplitudes exceeding 80 cm. Decreasing the time spacing of the air pressure forcing from 1 h to 3 h reduces the S 2 amplitude in air pressure by a factor of 0.82, consistent with expectations based on Fourier analysis. This highlights the importance of using hourly atmospheric forcing when predicting the global sea level response to atmospheric forcing. The radiational S 2 tide in sea level is subject to strong nonlinear interaction with the gravitational tide, leading to a pronounced attenuation of the radiational S 2 tide. The attenuation is explained by an increase in effective bottom friction at the S 2 frequency due to the presence of the gravitational tide. Four schemes for predicting TWL are evaluated to quantify the impact of tidal nudging and nonlinear interaction of tide and surge. Using TWLs observed by 304 coastal tide gauges, we show it is necessary to include both tidal nudging and nonlinear interaction. Plans for the further development of an operational flood forecast system for the Canadian coast, based on the above model, are discussed.

With the long-term goal of developing an ensemble forecast system for coastal flooding, we are developing a dynamically-based, numerical model of the global ocean. The model is based on the NEMO framework and has been used to predict global tides and surges in previous studies. This study focuses on the optimization of the joint prediction of both tides and surges, the two main components of total water level that cause coastal flooding. To improve the predictions of the tide we use a modified form of “spectral nudging”. We show this leads to significant improvements in the prediction of the M2 tide in the open ocean, and also in the shallow regions closer to shore where the model is not nudged. The median value of the vector difference of the tidal amplitude based on sea level observations and a data-assimilative model, and the predictions of our ocean model, is reduced from 11.2 cm to 2.66 cm by the nudging. The improvement deteriorates significantly however if additional tidal constituents are included in the model (most notably S2). This is explained in terms of spectral leakage between tidal bands associated with the nudging methodology and a straightforward solution is proposed.

Global M2 tidal surface currents are predicted using a global baroclinic ocean model with horizontal grid spacing of 1/12° and 19 z-levels in the vertical. After first showing the predicted tidal elevations are in reasonable agreement with observations made by bottom pressure recorders and altimeters, the predicted tidal surface currents are evaluated by comparing them with independent estimates based on observed drifter trajectories. Both predicted and observed tidal surface currents can exceed 0.1 m s−1 in the deep ocean. Internal tides are shown to make a significant contribution to the predicted tidal surface currents. Phase locking of the surface and internal tides causes spatial changes in the predicted tidal surface currents that vary with approximately the same wavenumber as that of the lowest mode internal tide. Qualitatively similar, small-scale variations are also detected in the observed estimates but the variations do not line up exactly with the predictions. Possible explanations for the mismatch are given. The seasonal variation of M2 tidal surface currents, and the energy conversion rate from surface to internal tides, is also predicted by initializing, and restoring, the model to an observed seasonal climatology of temperature and salinity. Compared to tidal elevation, the seasonal change of tidal surface current can be large (order 10% for each hemisphere). It is caused by seasonal variations in the vertical structure of the baroclinic modes and the energy conversion rate. In the vicinity of major bathymetric features, the seasonal variation of second and higher order modes can be much larger (up to 50%).

A global deterministic wave prediction system (GDWPS) is used to improve regional forecasts of waves off the Canadian coastline and help support the practice of safe marine activities in Canadian waters. The wave model has a grid spacing of ¼° with spectral resolution of 36 frequency bins and 36 directional bins. The wave model is driven with hourly 10-m winds generated by the operational global atmospheric prediction system. Ice conditions are updated every three hours using the ice concentration forecasts generated by the Global Ice–Ocean Prediction System. Wave forecasts are evaluated over two periods from 15 August to 31 October 2014 and from 15 December 2014 to 28 February 2015, as well as over select cases during the fall of 2012. The global system is shown to improve wave forecast skill over regions where forecasts were previously produced using limited-area models only. The usefulness of a global expansion is demonstrated for large swell events affecting the northeast Pacific. The first validation of a Canadian operational wave forecast system in the Arctic is presented. Improvements in the representation of forecast wave fields associated with tropical cyclones are also demonstrated. Finally, the GDWPS is shown to result in gains of at least 12 h of lead time.

The U.S. National Centers for Environmental Prediction (NCEP) and the Fleet Numerical Meteorology and Oceanography Center (FNMOC) have joined forces to establish a first global multicenter ensemble system dedicated to probabilistic forecasts of windwave heights. Both centers run independent wave ensemble systems (WES), which are combined onto a multicenter system with 41 members. A performance assessment of the multicenter wave-height product is made relative to altimeter data. Computed estimates of mean errors, ability to represent uncertainty, and reliability of probabilistic forecasts indicate that the multicenter ensemble product outperforms individual WES and deterministic wave models alike. The investigation includes an evaluation made at NCEP's National Hurricane Center (NHC) of the multicenter WES product, including severe sea-state events. The interagency collaboration has provided an opportunity to investigate in more depth the properties of wave ensembles, which has led to planned improvements ...

A dynamical model was experimentally implemented to provide high resolution forecasts at points of interests in the 2010 Vancouver Olympics and Paralympics Region. In a first experiment, GEM-Surf, the near surface and land surface modeling system, is driven by operational atmospheric forecasts and used to refine the surface forecasts according to local surface conditions such as elevation and vegetation type. In this simple form, temperature and snow depth forecasts are improved mainly as a result of the better representation of real elevation. In a second experiment, screen level observations and operational atmospheric forecasts are blended to drive a continuous cycle of near surface and land surface hindcasts. Hindcasts of the previous day conditions are then regarded as today’s optimized initial conditions. Hence, in this experiment, given observations are available, observation driven hindcasts continuously ensure that daily forecasts are issued from improved initial conditions. GEM-Surf forecasts obtained from improved short-range hindcasts produced using these better conditions result in improved snow depth forecasts. In a third experiment, assimilation of snow depth data is applied to further optimize GEM-Surf’s initial conditions, in addition to the use of blended observations and forecasts for forcing. Results show that snow depth and summer temperature forecasts are further improved by the addition of snow depth data assimilation.

As harvesting of wind energy grows, so does the need for improved forecasts from the surface to the top of wind turbines. To improve mesoscale forecasts of wind, temperature, and dewpoint temperature in this layer, two different approaches are examined. In the first experiment, the vertical resolution of a limited-area model with 2.5-km grid spacing (LAM-2.5 km) is significantly increased near the surface to better represent profiles in that layer. In the second experiment, prognostic variables for land and ocean surfaces are initialized using results from an external land surface model system [the Global Environmental Multiscale Surface system (GEM-Surf)] and from a regional ocean model. Results show that increasing the vertical resolution near the surface leads to improved temperature and dewpoint temperature forecasts at the surface and in the wind turbine layer. For winds, improvements are more modest, because they are limited to the gradient measured across the span of the vertical wind turbine blades. On the other hand, the replacement of operational surface analyses with high-resolution analyses obtained from GEM-Surf is found to improve summer dewpoint temperature forecasts. It is shown that changes in soil moisture analyses explain the bulk of the improved dewpoint forecasts.

A high-resolution 2D near-surface and land surface model was developed to produce snow and temperature forecasts over the complex alpine region of the Vancouver 2010 Winter Olympic and Paralympic Games. The model is driven by downscaled operational outputs from the Meteorological Service of Canada’s regional and global forecast models. Downscaling is applied to correct forcings for elevation differences between the operational forecast models and the high-resolution surface model. The high-resolution near-surface and land surface model is then used to further refine the forecasts. The model was validated against temperature and snow depth observations. The largest improvements were found in regions where low-resolution (i.e., on the order of 10 km or more) operational models typically lack the spatial resolution to capture rapid elevation changes. The model was found to better reproduce the intermittent snow cover at low-lying stations and to reduce snow depth error by as much as 3 m at alpine stations.

Estimation of the probability distribution of extreme sea levels, for the present time and the next century, is discussed. Two approaches are described and their strengths and weaknesses are compared. The first approach is based on dynamics and uses a storm surge model forced by tides, winds and air pressure fields. The second approach is based on the statistical analysis of observed hourly sea level records using a new first-order Markov process that can capture non-Gaussian characteristics (such as skewness) in the non-tidal component of the observed sea level record. It is shown that both approaches can provide good estimates of present day flooding probabilities for regions with relatively strong tides. The limitations of both approaches in terms of assessing the effect of global sea level rise, glacial-isostatic adjustment of the land, and changes in the frequency and severity of storms and hurricanes, are illustrated using recent results for the Northwest Atlantic. Some sensitivity studies are carried out to transform uncertainty in climate change projections into uncertainties in the probability of coastal flooding.

This study of extreme sea levels is motivated by concern over increased coastal erosion and flooding under plausible climate change scenarios. Extremal analyses are performed on the annual and seasonal maxima from 24 tide gauge stations in the Northwest Atlantic. At data poor locations, a 40 yr surge hindcast and information from short observation records are used to reconstruct sea level records prior to the annual and seasonal analysis of extremes. A Digital Elevation Model is used to generate spatial maps of the return period of extreme sea levels associated with specified flooding probabilities under current conditions and under projected global sea level rise scenarios for the next century. It is the first time such maps have been produced. Their primary advantage is that extreme sea levels are expressed in terms of inundated areas as opposed to a critical flood value about an arbitrary datum. Another novel aspect of this study is that the extremal analyses are carried out for specific seasons.

[1] Sea level observations and a dynamical model are used to investigate tide-surge interaction in the coastal waters off the east coast of Canada and northern USA. The study is motivated in part by the need to improve operational forecasts of total water level and coastal flooding. Two statistical methods are used to search for evidence of tide-surge interaction in hourly sea level records from 23 coastal locations. The methods are based on comparison of the statistical properties of the sea level residuals (observed sea level minus tide) occurring at different stages of the tidal cycle. While recognizing the limitations of such an approach, it is concluded that tide-surge interaction does occur in the Northumberland Strait which is located in the southern Gulf of St. Lawrence. Results for the Gulf of Maine and Bay of Fundy are less conclusive. A dynamical model is also used to quantify tide-surge interaction in the study region and to identify its physical causes. Tide-surge interaction in the model is strongest in the Northumberland Strait where the amplitude of the effect can reach 20 cm during and following strong storm surge events. This is large enough to be of practical significance in terms of flood forecasting. A series of sensitivity experiments with the model shows that the nonlinear parameterization of bottom stress is the principal contributor to tide-surge interaction.

[1] A 40 year hindcast of storm surges in the northwest Atlantic and adjacent shelf seas is performed using a 2-D nonlinear barotropic ocean model forced by realistic 6 hourly winds and air pressures. This hindcast is used to generate spatial maps of the return level of storm surges and also to estimate the return period of extreme total sea levels. The accuracy of the hindcast is assessed in two ways. First, the standard deviation of the difference between the observed residuals (total sea level minus tide) and the hindcast is calculated at 24 tide gauge locations. A typical error standard deviation is 8 cm. Second, the 40 year return level of observed residuals is compared to that of the hindcast surges. The predicted 40 year return levels are typically within 10 cm of observed return levels at the 24 observation locations. A spatial map of the 40 year return level of surges is presented for the northwest Atlantic. It identifies the regions exposed to the largest surges. Total sea levels are reconstructed using (1) the hindcast surges and (2) tides and higher-frequency variability predicted from short, observed sea level records. An extremal analysis of the reconstructed total sea levels shows that their 40 year return levels are in good agreement (within about 10 cm) with the levels calculated from multidecadal observed sea level records. This means that given a short record anywhere within the model domain, or results from a good tidal model, 40 year return levels can be estimated.

Regional deterministic and ensemble surge prediction systems (RDSPS and RESPS respectively) are used to forecast sea levels off the east of Canada and northeast US. The surge models for the RDSPS and RESPS have grid spacings of 1/30° and 1/12° respectively. The models are driven by surface air pressure and 10 m winds generated by operational global deterministic and ensemble prediction systems that are run operationally by the Canadian Meteorological Centre. Surge forecasts are evaluated for the period 1 March, 2013 to 31 March 2014. Based on traditional statistics (e.g., standard deviation of the difference between observations and predictions) both systems are shown to have skill in forecasting surges six days into the future. It is shown however that skill exists beyond six days if allowance is made for errors in the timing of large surges. The usefulness of the RESPS is demonstrated for two positive surges (important for coastal flooding and erosion) and a negative surge (important for safe navigation in shallow water). It is shown that the RESPS can identify events not forecast by the RDSPS, and can also add useful additional information on the timing of the surge, an important consideration in tidally dominated waters. Several new types of display are used to illustrate the sort of information that can be generated by the RESPS to support the issuers of warnings of unusually high and low total water levels.