Interannual Prediction System Research Articles

Skillful Long-Lead Seasonal Predictions in the Summertime Northern Hemisphere Midlatitudes

Abstract In contrast to boreal winter when extratropical seasonal predictions benefit greatly from ENSO-related teleconnections, our understanding of forecast skill and sources of predictability in summer is limited. Based on 40 years of hindcasts of the Canadian Seasonal to Interannual Prediction System, version 3 (CanSIPSv3), this study shows that predictions for the Northern Hemisphere summer surface air temperature are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. These midlatitude regions of statistically significant predictive skill appear to be connected to each other through an upper-tropospheric circumglobal wave train. Although a large part of the forecast skill for the surface air temperature and 500-hPa geopotential height is attributable to the linear trend associated with global warming, there is significant long-lead seasonal forecast skill related to interannual variability. Two additional idealized hindcast experiments are performed to help shed light on sources of the long-lead forecast skill using one of the CanSIPSv3 models and its uncoupled version. It is found that tropical ENSO-related sea surface temperature (SST) anomalies contribute to the forecast skill in the western United States, while land surface conditions in winter, including snow cover and soil moisture, in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill in these regions. This implies that improving land surface initial conditions and model representation of land surface processes is crucial for the further development of a seasonal forecasting system. Significance Statement Useful seasonal predictions in the boreal summer midlatitude regions are of great value. In this study, we show that predictions for the boreal summer season are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. The forecast skill in these regions is associated with a circumglobal teleconnection atmospheric circulation pattern. Sources of the long-lead forecast skill include the global warming–related trend and anomalies in the ocean and land surface initial conditions. It is found that the wintertime snow cover and soil moisture in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill.

Evaluation of Soil Moisture in the Canadian Seasonal to Interannual Prediction System, Version 2.1 (CanSIPSv2.1)

Abstract We evaluate the soil moisture hindcasts and the reconstruction runs giving the hindcasts initial conditions in version 2.1 of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2.1). Different strategies are used to initialize the hindcasts for the two CanSIPSv2.1 models, CanCM4i and the coupled Global Environmental Multiscale, version 5.1, (GEM5)–NEMO model (GEM5-NEMO), with contrasting impacts on the soil moisture initial conditions and forecast performance. Forecast correlation skill is decomposed into contributions from persistence of the initial anomalies and contributions not linked to persistence, with performance largely driven by the accuracy of the initial conditions in regions of strong persistence. Seasonal soil moisture correlation skill is significant for several months into the hindcasts depending on initial and target months, with contributions not linked to persistence becoming more notable at longer lead times. For the first 2–4 months, the quality of CanSIPSv2.1 ensemble mean forecasts tends to be higher on average during summer and fall and is comparable to that of the best performing model, whereas CanSIPSv2.1 outperforms the single models during spring and winter. For longer lead times, remote climate influences from the Pacific Ocean are notable and contribute to predictable soil moisture variability in teleconnected regions.

CanStoc: A Hybrid Stochastic–GCM System for Monthly, Seasonal and Interannual Predictions

Beyond their deterministic predictability limits of ≈10 days and 6 months, the atmosphere and ocean become effectively stochastic. This has led to the development of stochastic models specifically for this macroweather regime. A particularly promising approach is based on the Fractional Energy Balance Equation (FEBE), an update of the classical Budyko–Sellers energy balance approach. The FEBE has scaling symmetries that imply long memories, and these are exploited in the Stochastic Seasonal and Interannual Prediction System (StocSIPS). Whereas classical long-range forecast systems are initial value problems based on spatial information, StocSIPS is a past value problem based on (long) series at each pixel. We show how to combine StocSIPS with a classical coupled GCM system (CanSIPS) into a hybrid system (CanStoc), the skill of which is better than either. We show that for one-month lead times, CanStoc’s skill is particularly enhanced over either CanSIPS or StocSIPS, whereas for 2–3-month lead times, CanSIPS provides little extra skill. As expected, the CanStoc skill is higher over ocean than over land with some seasonal dependence. From the classical point of view, CanStoc could be regarded as a post-processing technique. From the stochastic point of view, CanStoc could be regarded as a way of harnessing extra skill at the submonthly scales in which StocSIPS is not expected to apply.

Improved Seasonal Forecast Skill of Pan-Arctic and Regional Sea Ice Extent in CanSIPS Version 2

Abstract This study assesses the forecast skill of the Canadian Seasonal to Interannual Prediction System (CanSIPS), version 2, in predicting Arctic sea ice extent on both the pan-Arctic and regional scales. In addition, the forecast skill is compared to that of CanSIPS, version 1. Overall, there is a net increase of forecast skill when considering detrended data due to the changes made in the development of CanSIPSv2. The most notable improvements are for forecasts of late summer and autumn target months that have been initialized in the months of April and May that, in previous studies, have been associated with the spring predictability barrier. By comparison of the skills of CanSIPSv1 and CanSIPSv2 to that of an intermediate version of CanSIPS, CanSIPSv1b, we can attribute skill differences between CanSIPSv1 and CanSIPSv2 to two main sources. First, an improved initialization procedure for sea ice initial conditions markedly improves forecast skill on the pan-Arctic scale as well as regionally in the central Arctic, Laptev Sea, Sea of Okhotsk, and Barents Sea. This conclusion is further supported by analysis of the predictive skill of the sea ice volume initialization field. Second, the change in model combination from CanSIPSv1 to CanSIPSv2 (exchanging the constituent CanCM3 model for GEM-NEMO) improves forecast skill in the Bering, Kara, Chukchi, Beaufort, East Siberian, Barents, and the Greenland–Iceland–Norwegian (GIN) Seas. In Hudson and Baffin Bay, as well as the Labrador Sea, there is limited and unsystematic improvement in forecasts of CanSIPSv2 as compared to CanSIPSv1.

Seasonal Predictions of Regional and Pan-Antarctic Sea Ice With a Dynamical Forecast System

ABSTRACT Operational seasonal to interannual forecasting systems are in continued development around the world. Various studies have applied models to the dynamical forecasting of sea ice, particularly in the Arctic. The Antarctic, however, has received relatively little attention, with few previous endeavours to quantify operational forecast skill of sea ice. This study assesses sea ice extent prediction skill of the Canadian Seasonal to Interannual Prediction System version 2 (CanSIPSv2) in the Pan-Antarctic domain as well as in various sectors of the Southern Ocean. The forecast skill of GEM-NEMO, one of two constituent models that together comprise CanSIPSv2, is found to generally exceed that of the other, CanCM4i. This difference is potentially due to substantial model drift of sea ice extent away from observations in CanCM4i, in addition to their different initializations of sea ice thickness. Both models show significant forecast skill exceeding that of an anomaly persistence forecast. Prediction skill was found to vary substantially across different sectors of the Southern Ocean. Moreover, our analysis also finds that CanSIPSv2 forecast skill in the Antarctic shows a dependence on time period, demonstrating generally lower skill than seen in the Arctic over the years 1980–2010, in contrast to generally higher skill than in the Arctic over the years 1980–2019.

Long‐Range Forecasting as a Past Value Problem: Untangling Correlations and Causality With Scaling

AbstractConventional long‐range weather prediction is an initial value problem that uses the current state of the atmosphere to produce ensemble forecasts. Purely stochastic predictions for long‐memory processes are “past value” problems that use historical data to provide conditional forecasts. Teleconnection patterns, defined from cross‐correlations, are important for identifying possible dynamical interactions, but they do not necessarily imply causation. Using the precise notion of Granger causality, we show that for long‐range stochastic temperature forecasts, the cross‐correlations are only relevant at the level of the innovations–not temperatures. This justifies the Stochastic Seasonal to Interannual Prediction System (StocSIPS) that is based on a (long memory) fractional Gaussian noise model. Extended here to the multivariate case (m‐StocSIPS) produces realistic space‐time temperature simulations. Although it has no Granger causality, emergent properties include realistic teleconnection networks and El Niño events and indices.

Using regional scaling for temperature forecasts with the Stochastic Seasonal to Interannual Prediction System (StocSIPS)

Over time scales between 10 days and 10–20 years—the macroweather regime—atmospheric fields, including the temperature, respect statistical scale symmetries, such as power-law correlations, that imply the existence of a huge memory in the system that can be exploited for long-term forecasts. The Stochastic Seasonal to Interannual Prediction System (StocSIPS) is a stochastic model that exploits these symmetries to perform long-term forecasts. It models the temperature as the high-frequency limit of the (fractional) energy balance equation, which governs radiative equilibrium processes when the relevant equilibrium relaxation processes are power law, rather than exponential. They are obtained when the order of the relaxation equation is fractional rather than integer and they are solved as past value problems rather than initial value problems. StocSIPS was first developed for monthly and seasonal forecast of globally averaged temperature. In this paper, we extend it to the prediction of the spatially resolved temperature field by treating each grid point as an independent time series. Compared to traditional global circulation models (GCMs), StocSIPS has the advantage of forcing predictions to converge to the real-world climate. It extracts the internal variability (weather noise) directly from past data and does not suffer from model drift. Here we apply StocSIPS to obtain monthly and seasonal predictions of the surface temperature and show some preliminary comparison with multi-model ensemble (MME) GCM results. For 1 month lead time, our simple stochastic model shows similar—but somewhat higher—values of the skill scores than the much more complex deterministic models.

Development and Calibration of Seasonal Probabilistic Forecasts of Ice-Free Dates and Freeze-Up Dates

AbstractDynamical forecasting systems are being used to skillfully predict deterministic ice-free and freeze-up date events in the Arctic. This paper extends such forecasts to a probabilistic framework and tests two calibration models to correct systematic biases and improve the statistical reliability of the event dates: trend-adjusted quantile mapping (TAQM) and nonhomogeneous censored Gaussian regression (NCGR). TAQM is a probability distribution mapping method that corrects the forecast for climatological biases, whereas NCGR relates the calibrated parametric forecast distribution to the raw ensemble forecast through a regression model framework. For NCGR, the observed event trend and ensemble-mean event date are used to predict the central tendency of the predictive distribution. For modeling forecast uncertainty, we find that the ensemble-mean event date, which is related to forecast lead time, performs better than the ensemble variance itself. Using a multidecadal hindcast record from the Canadian Seasonal to Interannual Prediction System (CanSIPS), TAQM and NCGR are applied to produce categorical forecasts quantifying the probabilities for early, normal, and late ice retreat and advance. While TAQM performs better than adjusting the raw forecast for mean and linear trend bias, NCGR is shown to outperform TAQM in terms of reliability, skill, and an improved tendency for forecast probabilities to be no worse than climatology. Testing various cross-validation setups, we find that NCGR remains useful when shorter hindcast records (~20 years) are available. By applying NCGR to operational forecasts, stakeholders can be more confident in using seasonal forecasts of sea ice event timing for planning purposes.

Comparing the Assimilation of SMOS Brightness Temperatures and Soil Moisture Products on Hydrological Simulation in the Canadian Land Surface Scheme

Soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Therefore, there is widespread interest in the use of soil moisture retrievals from passive microwave satellites. In the assimilation of satellite soil moisture data into land surface models, two approaches are commonly used. In the first approach brightness temperature (TB) data are assimilated, while in the second approach retrieved soil moisture (SM) data from the satellite are assimilated. However, there is not a significant body of literature comparing the differences between these two approaches, and it is not known whether there is any advantage in using a particular approach over the other. In this study, TB and SM L2 retrieval products from the Soil Moisture and Ocean Salinity (SMOS) satellite are assimilated into the Canadian Land Surface Scheme (CLASS), for improved soil moisture estimation over an agricultural region in Saskatchewan. CLASS is the land surface component of the Canadian Earth System Model (CESM), and the Canadian Seasonal and Interannual Prediction System (CanSIPS). Our results indicated that assimilating the SMOS products improved the soil moisture simulation skill of the CLASS. Near surface soil moisture assimilation also resulted in improved forecasts of root zone soil moisture (RZSM) values. Although both techniques resulted in improved forecasts of RZSM, assimilation of TB resulted in the superior estimates.

The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2)

AbstractThe second version of the Canadian Seasonal to Interannual Prediction System (CanSIPSv2) was implemented operationally at Environment and Climate Change Canada (ECCC) in July 2019. Like its predecessors, CanSIPSv2 applies a multimodel ensemble approach with two coupled atmosphere–ocean models, CanCM4i and GEM-NEMO. While CanCM4i is a climate model, which is upgraded from CanCM4 of the previous CanSIPSv1 with improved sea ice initialization, GEM-NEMO is a newly developed numerical weather prediction (NWP)-based global atmosphere–ocean coupled model. In this paper, CanSIPSv2 is introduced, and its performance is assessed based on the reforecast of 30 years from 1981 to 2010, with 10 ensemble members of 12-month integrations for each model. Ensemble seasonal forecast skill of 2-m air temperature, 500-hPa geopotential height, precipitation rate, sea surface temperature, and sea ice concentration is assessed. Verification is also performed for the Niño-3.4, the Pacific–North American pattern (PNA), the North Atlantic Oscillation (NAO), and the Madden–Julian oscillation (MJO) indices. It is found that CanSIPSv2 outperforms the previous CanSIPSv1 system in many aspects. Atmospheric teleconnections associated with the El Niño–Southern Oscillation (ENSO) are reasonably well captured by the two CanSIPSv2 models, and a large part of the seasonal forecast skill in boreal winter can be attributed to the ENSO impact. The two models are also able to simulate the Northern Hemisphere teleconnection associated with the tropical MJO, which likely provides another source of skill on the subseasonal to seasonal time scale.

Predicting the global temperature with the Stochastic Seasonal to Interannual Prediction System (StocSIPS)

Many atmospheric fields—in particular the temperature—respect statistical symmetries that characterize the macroweather regime, i.e. time-scales between the $$\approx$$ 10 day lifetime of planetary sized structures and the (currently) 10–20 year scale at which the anthropogenic forcings begin to dominate the natural variability. The scale-invariance and the low intermittency of the fluctuations implies the existence of a huge memory in the system that can be exploited for macroweather forecasts using well-established (Gaussian) techniques. The Stochastic Seasonal to Interannual Prediction System (StocSIPS) is a stochastic model that exploits these symmetries to perform long-term forecasts. StocSIPS includes the previous ScaLIng Macroweather Model (SLIMM) as a core model for the prediction of the natural variability component of the temperature field. Here we present the theory for improving SLIMM using discrete-in-time fractional Gaussian noise processes to obtain an optimal predictor as a linear combination of past data. We apply StocSIPS to the prediction of globally-averaged temperature and confirm the applicability of the model with statistical testing of the hypothesis and a good agreement between the hindcast skill scores and the theoretical predictions. Finally, we compare StocSIPS with the Canadian Seasonal to Interannual Prediction System. From a forecast point of view, GCMs can be seen as an initial value problem for generating many “stochastic” realizations of the state of the atmosphere, while StocSIPS is effectively a past value problem that estimates the most probable future state from long series of past data. The results validate StocSIPS as a good alternative and a complementary approach to conventional numerical models. Temperature forecasts using StocSIPS are published on a regular basis in the website: http://www.physics.mcgill.ca/StocSIPS/ .

Initialization and Potential Predictability of Soil Moisture in the Canadian Seasonal to Interannual Prediction System

The initialization and potential predictability of soil moisture in CanCM4 hindcasts during 1981–2010 is assessed. CanCM4 is one of the two global climate models employed by the Canadian Seasonal to Interannual Prediction System (CanSIPS) providing operational multiseasonal forecasts for Environment and Climate Change Canada (ECCC). Soil moisture forecast initialization in CanSIPS is determined by the response of the land component to forcing from data-constrained model atmospheric fields. We evaluate hindcast initial conditions for soil moisture and its atmospheric forcings against observation-based datasets. Although model values of soil moisture variability compare relatively well with a blend of two reanalysis products, there is significant disagreement in the tropics and arid regions linked to biases in precipitation, as well as in snow-covered regions, likely the result of biases in the timing of snow onset and melt. The temporal variance of initial soil moisture anomalies is typically larger in regions of considerable precipitation variability and in cold continental areas of shallow soil depth. Appreciable variance of initial conditions, combined with persistence of the initial anomalies and the model’s ability to represent future climate variations, lead to potentially predictable soil moisture variance exceeding 60% of the total variance for up to 3–4 months in the tropics and 6–7 months in the mid- to high latitudes during hemispheric winter. Potential predictability at longer leads is primarily found in the tropics and extratropical areas of ENSO-teleconnected influences. We use lagged partial correlations to show that ENSO-teleconnected precipitation in CanCM4 is a likely source of potential predictability of soil moisture up to 1-yr lead in CanSIPS hindcasts.

Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system

Abstract. The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate-prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time.

Influence of snowmelt on soil moisture and on near surface air temperature during winter–spring transition season

This study examines relationships between snowmelt and soil moisture (SM), in particular, the influence of snowmelt on soil moisture memory (SMM) and on near surface air temperature (T2M) over the extra-tropical northern hemisphere (ENH) using four state-of-the-art reanalysis products: ERA-Interim, ERA-Interim Land, MERRA-Land, and GLDAS, as well as using Canadian Seasonal and Interannual Prediction System (CanSIPS) seasonal hindcast data, over a 20 year period (1986–2005). We use correlation-based metrics along with a simple classification-based on when the top layer soil temperature ( $$T_g$$ ) rises above freezing point during the annual freeze–thaw season, to evaluate the influence of snowmelt on SM. Our results show considerable differences across reanalyses as well as CanSIPS hindcasts regarding timing of maximum SWE ( $${\rm{SWE}}_{\rm{max}}$$ ) occurrences as well as the onset of thawing of the frozen soil. Correlation statistics indicate that $${\rm{SWE}}_{\rm{max}}$$ strongly influences SM. As a measure of the persistence of this relationship, a decay time is defined by lag in days over which the correlation of SM with lagged $${\rm{SWE}}_{\rm{max}}$$ decays to 1/e of its peak value. For a majority of grid cells over ENH this decay time is less than 45 days, which suggests $${\rm{SWE}}_{\rm{max}}$$ does not strongly influence the SM beyond subseasonal time scales. The interannual autocorrelation of SM indicates strong persistence over subseasonal time scales, consistently across reanalyses as well as CanSIPS hindcasts. However, intra-seasonal autocorrelations of ERA-Interim and MERRA-Land SM over North America show anomalous sudden decline of SMM compared to the other products, likely due to the offline forcing of atmospheric variables which blocks the atmosphere’s response to land feedbacks. One of the models used in CanSIPS, the Canadian Climate Model version 4 (CanCM4), also shows a sudden decline of intra-seasonal autocorrelation over Central Asia which is most likely due to weak land–atmosphere coupling over the region. Lag–lead correlation statistics between SM and T2M during the soil thaw period suggests that SM anomalies have measurable lagged influence on T2M with varying decay time over different regions and across different datasets.

A Postprocessing Method for Seasonal Forecasts Using Temporally and Spatially Smoothed Statistics

AbstractA statistical postprocessing method for seasonal forecasts based on temporally and spatially smoothed climate statistics is introduced. The method uses information available from seasonal hindcasts initialized at the beginning of 12 calendar months. The performance of the method is tested within both deterministic and probabilistic frameworks using output from the ensemble of seasonal hindcasts produced by the Canadian Seasonal to Interannual Prediction System for the 30-yr period 1981–2010. Forecast skill improvements are found to be greater when forecast adjustment parameters estimated for individual seasons and at individual grid points are temporally and spatially smoothed. The greatest skill improvements are typically achieved for seasonally invariant parameters while skill improvements due to additional spatial smoothing are modest.

Representation of Snow in the Canadian Seasonal to Interannual Prediction System. Part II: Potential Predictability and Hindcast Skill

Abstract This paper examines potential predictability (PP) and actual skill for snow water equivalent (SWE) in the Canadian Seasonal to Interannual Prediction System (CanSIPS). A significant PP is found for SWE, with potentially predictable variance over 50% of the total variance at up to a 5-month lead in mid- to high latitudes in forecasts initialized after snow onset. Much, though not all, of this PP stems from a tendency for SWE anomalies to persist through the snow season. Although the spring melt acts as a PP barrier regardless of initialization date, in some regions significant PP reemerges in the following snow season. This is due primarily to ENSO teleconnections that are modeled realistically by CanSIPS, particularly in northwestern North America. Actual skill of CanSIPS in forecasting SWE is assessed using several verification datasets. Highest skills are obtained using a blend of five such datasets, consistent with the hypothesis that skill scores are degraded by errors in the verification data as well as by forecast errors, and that observational errors can be reduced by blending multiple datasets, much as forecast errors can be reduced by averaging across different models. Actual skill for SWE is comparable to, though generally lower than, that implied by PP. This is due in part to the similar autocorrelation properties of the forecast and observed SWE anomalies, which provide skill through anomaly persistence, combined with reasonably accurate initialization of SWE by CanSIPS. Long-lead skill across snow seasons is found to be linked to ENSO, particularly in western North America, much as for PP.

Representation of Snow in the Canadian Seasonal to Interannual Prediction System. Part I: Initialization

Abstract The ability of the Canadian Seasonal to Interannual Prediction System (CanSIPS) to provide realistic forecast initial conditions for snow cover is assessed using in situ measurements and gridded snow analyses. Forecast initial conditions for snow in CanCM3 and CanCM4 employed by CanSIPS are determined by the response of the Canadian Land Surface Scheme (CLASS) used in both models to forcing from model atmospheric fields constrained by assimilation of 6-hourly reanalysis data. These snow initial conditions are found to be representative of the daily climatology of snow water equivalent (SWE) as well as interannual variations in maximum SWE and the timing of snow onset and snowmelt observed at eight in situ measurement sites located across Canada. The level of this agreement is similar to that of three independent gridded snow analyses (MERRA, the European Space Agency’s GlobSnow, and an offline forced version of CLASS). Total Northern Hemisphere snow mass generated by the CanSIPS initialization procedure is larger for both models (especially CanCM3) than in MERRA, mostly because of higher SWE in regions of common snow cover. Globally, the interannual variability of initial SWE is found to correlate highly with that of MERRA in locations with appreciable snow. These initial values are compared to SWE in freely running CanCM3 and CanCM4 simulations produced without data assimilation of atmospheric fields. Differences in climatological SWE relative to MERRA are similar in the freely running and assimilating CanCM3 and CanCM4 simulations, suggesting that inherent model biases are a major contributor to biases in CanSIPS snow initial conditions.

Real‐time estimation of Arctic sea ice thickness through maximum covariance analysis

AbstractA challenge for model‐based seasonal predictions of sea ice is an accurate representation of sea ice initial conditions, particularly sparsely observed sea ice thickness (SIT). The Canadian Seasonal to Interannual Prediction System (CanSIPS) currently initializes SIT by nudging simulated values toward a model‐based climatology. To improve on this, we use sea ice data from Pan‐Arctic Ice Ocean Modeling and Assimilation System to investigate how accurately SIT can be estimated in real time using better observed and physically relevant predictors. We (1) test the skill of several predictors using maximum covariance analysis (MCA), (2) apply an approach which blends sea ice concentration and lagged (4 month averaged) sea level pressure, and (3) compare this method against the current CanSIPS initialization scheme over 1981–2012. The MCA‐based statistical model reduces SIT areal mean and temporal mean absolute errors by 48% relative to the current CanSIPS initialization and shows consistent skill estimating ice volume in all months (r = 0.95).

A Dynamical Climate Model–Driven Hydrologic Prediction System for the Fraser River, Canada

Abstract Recent improvements in forecast skill of the climate system by dynamical climate models could lead to improvements in seasonal streamflow predictions. This study evaluates the hydrologic prediction skill of a dynamical climate model–driven hydrologic prediction system (CM-HPS), based on an ensemble of statistically downscaled outputs from the Canadian Seasonal to Interannual Prediction System (CanSIPS). For comparison, historical and future climate traces–driven ensemble streamflow prediction (ESP) was employed. The Variable Infiltration Capacity model (VIC) hydrologic model setup for the Fraser River basin, British Columbia, Canada, was used as a test bed for the two systems. In both cases, results revealed limited precipitation prediction skill. For streamflow prediction, the ESP approach has very limited or no correlation skill beyond the months influenced by initial hydrologic conditions, while the CM-HPS has moderately better correlation skill, attributable to the enhanced temperature prediction skill that results from CanSIPS’s ability to predict El Niño–Southern Oscillation (ENSO) and its teleconnections. The root-mean-square error, bias, and categorical skills for the two methods are mostly similar. Hydrologic modeling uncertainty also affects the prediction skill, and in some cases prediction skill is constrained by hydrologic model skill. Overall, the CM-HPS shows potential for seasonal streamflow prediction, and further enhancements in climate models could potentially to lead to more skillful hydrologic predictions.

Skill evaluation of the CanCM4 and its MOS for seasonal rainfall forecast in Malaysia during the early and late winter monsoon periods

This study evaluates the forecast skill of the fourth version of the Canadian coupled ocean–atmosphere general circulation model (CanCM4) and its model output statistics (MOS) to forecast the seasonal rainfall in Malaysia, particularly during early (October–November–December) and late (January–February–March) winter monsoon periods. CanCM4 is the latest component of the Canadian Seasonal to Inter-annual Prediction System (CanSIPS), which is a multi-seasonal climate prediction system developed particularly for Canada but applicable globally. Generally, CanCM4's skill in reproducing the climatology during winter is not as good as in other seasons because of the model's inability to simulate the regional synoptic circulations over the western Maritime Continent. In particular, the model fails to forecast the cold surges and Borneo vortex circulations that play critical roles in moisture horizontal advection. Moreover, its forecast skill during the early winter monsoon period is poorer than during the late period. Interestingly, forecast skill is enhanced when MOS models are applied as the MOS utilizes the predictive signals in the quasi-global predictors from the CanCM4 forecast system. The predictability can be traced to the conventional El Nino–Southern Oscillation (ENSO) and ENSO Modoki signals that are present in the CanCM4 forecast MOS predictor fields. The quasi-global sea-surface temperature and quasi-global sea-level pressure fields are found to be the most useful predictors. Interestingly, CanCM4 forecast signals associated with the Indian Ocean Dipole also contribute to the skill. Skill enhancement is particularly significant for northern Borneo during early monsoon periods in medium- and long-lead forecasts when the CanCM4 has minimal direct skill in the region.