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Improving Climate Models and Projections Using Observations Workshop 2023 What: More than 70 experts from research and operational centers came together in a hybrid meeting held at the Massachusetts Institute [...]

This study aims to evaluate the impact of the in-situ ocean observations on seasonal forecasts. A series of seasonal reforecasts have been conducted for the period 1993-2015, in which different sets of ocean observations were withdrawn in the production of the ocean initial conditions, while maintaining a strong constrain in sea surface temperature (SST). By comparing the different reforecast sets, it is possible to assess the impact on the forecast of ocean and atmospheric variables. Results show that the in-situ observations have profound and significant impacts on the mean state of forecast ocean and atmospheric variables, which can be classified into different categories: i) impact due to local air-sea interaction, as direct consequence of changes in the mixed layer in the ocean initial conditions, and visible in the early stages of the forecasts; ii) changes due to different ocean dynamical balances, most visible in the Equatorial Pacific in forecasts initialized in May, which amplify and evolve with forecast lead time; iii) changes to the atmospheric circulation resulting from changes in large scale SST gradients; these are non-local, mediated by the atmospheric bridge, and they are obvious from the visible impact of the removing in-situ observations on the Atlantic basin only in the global atmospheric circulation; iv) changes in the atmospheric tropical deep convection associated with the structure of the warm pools. The ocean observations have also a significant impact on the representation of the trends of the ocean initial conditions, which affect the trends in the seasonal forecasts of ocean and atmospheric variables. The impact of the ocean observing system in the Atlantic and extratropics appears dominated by Argo, but this is not the case in the Tropical Pacific, where the other ocean observing systems play a role in constraining the ocean state. [ABSTRACT FROM AUTHOR]

Understanding the predictability drivers for the North Atlantic Oscillation (NAO) during boreal winter at seasonal time scales remains challenging. This study uses large ensembles with the ECMWF seasonal forecasting system to investigate the relative impact of tropical Indian and Pacific heating on NAO predictability by examining the tropical forcing, teleconnection pathways, and sources of uncertainty. We select three case studies ‐ 1997/98, 2015/16 and 2019/20 ‐ with strong Indian Ocean heating anomalies, but with different El Niño conditions. We show that in 2019/20, with neutral ENSO conditions, Indian Ocean SSTs favor a positive NAO response via stratospheric and tropospheric pathways. In the cases with strong El Niño, we find contrasting results: in 1997/98, the Pacific forcing dominates, producing a negative NAO. In 2015/16, despite the strong El Niño, the Indian Ocean forcing dominates, leading to a positive NAO via intensification of the stratospheric polar vortex (SPV). While the stratospheric pathway exhibits varying responses to Indian Ocean forcing ‐ being weaker in 1997/98 and strongest in 2015/16, the Indian Ocean‐related tropospheric pathway remains robust along the Pacific subtropical jet across years. However, there is destructive interference between teleconnections from Indian and Pacific SST anomalies in both the tropospheric and stratospheric pathways. The competing effects of tropical heating in both basins, uncertainties in the Rossby wave response to tropical heating and SPV variability contribute to uncertainty in seasonal NAO predictions. The flow‐dependent nature of the stratospheric pathway underscores the complexity of seasonal forecast predictability, and the existence of windows of opportunity. Plain Language Summary: The NAO is a phenomenon that manifests as a pressure see‐saw in the North Atlantic, affecting the seasonal climate in many parts of the northern hemisphere. In this study, we demonstrate that the predictability of the phases of the NAO can be affected by the remote and opposing influence of surface temperatures in the tropical Indian and Pacific oceans, which propagate via the troposphere and stratosphere. This interplay, in turn, contributes to the uncertainty of seasonal forecasts of the NAO. Key Points: Large ensemble seasonal forecasts reveal a predictable link between tropical heating and the NAO via a flow‐dependent stratospheric pathwayThe impact of Indian Ocean heating on the NAO can be stronger than that of El Niño in the presence of an anomalously strong stratospheric pathwayCompetition between basins, flow‐dependent response, and intrinsic variability contribute to the uncertainty in seasonal forecasts of NAO [ABSTRACT FROM AUTHOR]

We assess for the first time the impact of in-situ ocean observations on European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal forecasts of both ocean and atmospheric conditions. A series of coupled reforecasts have been conducted for the period 1993-2015, in which different sets of ocean observations were withdrawn in the production of the ocean initial conditions. Removal of all ocean in-situ observations in the initial conditions leads to significant degradation in the forecasts of ocean surface and subsurface mean state at lead times from week 1 to week 4. The negative impact is predominantly caused by the removal of the Argo observing system in recent decades. Changes in the mean state of atmospheric variables are comparatively small but significant in the forecasts of lower and upper atmospheric circulation over large regions. Our results highlight the value of continuous, real-time in-situ observations of the surface and subsurface ocean for coupled forecasts in the sub-seasonal range. [ABSTRACT FROM AUTHOR]

There is growing demand for seasonal forecast products for marine applications. The availability of consistent and sufficiently long observational records of ocean variables permits the assessment of the spatial distribution of the skill of ocean variables from seasonal forecasts. Here we use state-of-the-art temporal records of sea surface temperature (SST), sea surface height (SSH) and upper 300m ocean heat content (OHC) to quantify the distribution of skill, up to 2 seasons ahead, of two operational seasonal forecasting systems contributing to the seasonal multi-model of the Copernicus Climate Change Services (C3S). This study presents the spatial distribution of the skill of the seasonal forecast ensemble mean in terms of anomaly correlation and root mean square error and compares it to the persistence and climatological benchmarks. The comparative assessment of the skill among variables sheds light on sources/ limits of predictability at seasonal time scales, as well as the nature of model errors. Beyond these standard verification metrics, we also evaluate the ability of the models to represent the observed long-term trends. Results show that long-term trends contribute to the skill of seasonal forecasts. Although the forecasts capture the long-term trends in general, some regional aspects remain challenging. Part of these errors can be attributed to specific aspects of the ocean initialization, but others, such as the overestimation of the warming in the Eastern Pacific are also influenced by model error. Skill gains can be obtained by improving the trend representation in future forecasting systems. In the meantime, a forecast calibration procedure that corrects the linear trends can produce substantial skill gains. The results show that calibrated seasonal forecasts beat both the climatological and persistence benchmark almost at every location for all initial dates and lead times. Results demonstrate the value of the seasonal forecasts for marine applications and highlight the importance of representing the decadal variability and trends in ocean heat content and sea level. [ABSTRACT FROM AUTHOR]

The European Centre for Medium‐range Weather Forecasts (ECMWF) extended‐range forecasts display large errors in the representations of Madden–Julian Oscillation (MJO) teleconnections over the North Atlantic with a strong underestimation of the impact of the MJO on the North Atlantic Oscillation (NAO) following an MJO in Phase 2–3. The origin of this error was investigated using a large set of re‐forecasts covering 20 years where part of the atmosphere was relaxed towards the ECMWF Reanalysis v5 (ERA5) reanalysis. These relaxation experiments show that relaxing the Tropics significantly improves the MJO teleconnections associated with Phase 2–3, with a reduction of about 50% in the amplitude error of the teleconnections. However, model errors outside the Tropics also play an important role. Results also suggest that errors in local processes reduce by about 20% the amplitude of the MJO teleconnections over the North Atlantic. Another example of a source of MJO teleconnection errors is the near surface over the Tibetan and Mongolian Plateaux. Nudging this area towards ERA5 improves the representation of the Pacific subtropical jet and the amplitude of MJO teleconnections associated with Phase 2–3 in the extended‐range re‐forecasts. Nudging the stratosphere exerts a comparatively weaker impact on the MJO teleconnections 11–15 days after an MJO in Phase 2–3. Overall, these experiments indicate that there are multiple sources of MJO teleconnection errors, making the representation of realistic MJO teleconnection by dynamical models a particularly challenging task. [ABSTRACT FROM AUTHOR]

A unique set of relaxation experiments with a forecast model initialized during December–January 1999–2019 is used to explore tropical influence on the Northern Hemisphere polar stratosphere and stratosphere–troposphere coupling, to quantify predictability benefits due to the perfect knowledge of the tropical variability. On average, predictability of the polar stratosphere, as represented by the 50 hPa geopotential height anomalies north of 50°N (Z50), increases from 17 days in freely running (control) forecasts to 21 days in the tropical relaxation experiments. At sub‐seasonal time‐scales, a statistically significant improvement in weekly mean skill scores can be demonstrated in 14%–20% of individual forecast ensembles, mostly in cases when the skill of the corresponding control forecast is worse than average. In these forecasts, root‐mean‐square errors and forecast spread of Z50 during forecast weeks 3–5 are decreased by 10%–15%. Stratospheric improvements are detected during periods of both vortex strengthening and vortex weakening, including most major Sudden Stratospheric Warmings that occurred during the study period, via modulation of the upward wave activity fluxes. An active Madden–Julian Oscillation (MJO) is found in most of these events with MJO phase 5–7 preceding vortex weakening and MJO phase 3–4 preceding vortex strengthening. Forecasts with improved stratospheric circulation also have improved tropospheric circulation during and after the periods when improvements are detected in the stratosphere. We attribute these improvements to both stratosphere–troposphere coupling and tropospheric tropical teleconnections. [ABSTRACT FROM AUTHOR]

Realistic representation of monthly sea level anomalies in coastal regions has been a challenge for global ocean reanalyses. This is especially the case in coastal regions where sea levels are influenced by western boundary currents such as near the U.S. Atlantic Coast and the Gulf of Mexico. For these regions, most ocean reanalyses compare poorly to observations. Problems in reanalyses include errors in data assimilation and horizontal resolutions that are too coarse to simulate energetic currents like the Gulf Stream and Loop Current System. However, model capabilities are advancing with improved data assimilation and higher resolution. Here, we show that some current-generation ocean reanalyses produce monthly sea level anomalies with improved skill when compared to satellite altimetry observations of sea surface heights. Using tide gauge observations for coastal verification, we find the highest skill associated with the GLORYS12 and HYCOM ocean reanalyses. Both systems assimilate altimetry observations and have eddy-resolving horizontal resolutions (1/12°). We found less skill in three other ocean reanalyses (ACCESS-S2, ORAS5, and ORAP6) with coarser, though still eddy-permitting, resolutions (1/4°). The operational reanalysis from ECMWF (ORAS5) and their pilot reanalysis (ORAP6) provide an interesting comparison because the latter assimilates altimetry globally and with more weight, as well as assimilating ocean observations over continental shelves. We find these attributes associated with improved skill near many tide gauges. We also assessed an older reanalysis (CFSR), which has the lowest skill likely due to its lower resolution (1/2°) and lack of altimetry assimilation. ACCESS-S2 likewise does not assimilate altimetry, although its skill is much better than CFSR and only somewhat lower than ORAS5. Since coastal flooding is influenced by sea level anomalies, the recent development of skilful ocean reanalyses on monthly timescales may be useful for better understanding the physical processes associated with flood risks. [ABSTRACT FROM AUTHOR]

Marine heatwaves (MHWs), defined as prolonged period of extremely warm sea surface temperature (SST), have been receiving a lot of attention in the past decade as their frequency and intensity increase in a warming climate. This paper investigates the extent to which the seasonal occurrence and duration of MHWs can be predicted with the European Centre for Medium-Range Weather Forecast (ECMWF) operational seasonal forecast system. The prediction of the occurrence of MHW events, the number of MHW days per season, and their intensity and spatial extent are derived from seasonal SST forecasts and evaluated against an observation-based SST analysis using both deterministic and probabilistic metrics over the 1982–2021 period. Forecast scores show useful skill in predicting the occurrence of MHWs globally for the two seasons following the starting date. The skill is the highest in the El Niño region, the Caribbean, the wider tropics, the north-eastern extra-tropical Pacific, and southwest of the extra-tropical basins. The skill is not as good for other midlatitude eastern basins nor for the Mediterranean, with the forecast system being able to represent the low-frequency modulation of MHWs but showing poor skill in predicting the interannual variability of the MHW characteristics. Linear trend analysis shows an increase in MHW occurrence at a global scale, which the forecasts capture well. [ABSTRACT FROM AUTHOR]

The tropical Pacific plays an important role in modulating the global climate through its prevailing sea-surface temperature spatial structure and dominant climate modes like El Niño-Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), and their teleconnections. These modes of variability, including their oceanic anomalies, are considered to provide sources of prediction skill on subseasonal timescales in the Tropics. Therefore, this study aims to examine how assimilating in situ ocean observations influences the initial ocean sea-surface temperature (SST) and mixed-layer depth (MLD) and their subseasonal forecasts. We analyze two subseasonal forecast systems generated with the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), where the ocean states were initialized using two Observing-System Experiment (OSE) reanalyses. We find that the SST differences between forecasts with and without ocean data assimilation grow with time, resulting in a reduced cold-tongue bias when assimilating ocean observations. Two mechanisms related to air-sea coupling are considered to contribute to this growth of SST differences. One is a positive feedback between the zonal SST gradient, pressure gradient, and surface wind. The other is the difference in Ekman suction and mixing at the Equator due to surface wind-speed differences. While the initial mixed-layer depth (MLD) can be improved through ocean data assimilation, this improvement is not maintained in the forecasts. Instead, the MLD in both experiments shoals rapidly at the beginning of the forecast. These results emphasize how initialization and model biases influence air-sea interaction and the accuracy of subseasonal forecasts in the tropical Pacific. [ABSTRACT FROM AUTHOR]

Marine Heatwaves (MHWs), defined as prolonged period of extremely warm Sea Surface Temperature (SST), have been receiving a lot of attention in the past decade as their frequency and intensity increase in a warming climate. This paper investigates the extent to which the seasonal occurrence and duration of MHWs can be predicted with the European Centre for Medium-range Weather Forecast (ECMWF) operational seasonal forecast system. The prediction of the occurrence of MHW events, the number of MHW days per season, their intensity and spatial extent is derived from SST seasonal forecasts and evaluated against an observation-based SST analysis using both deterministic and probabilistic metrics over the 1982-2021 period. Forecast scores show useful skill in predicting the occurrence of MHWs globally for the two seasons following the starting date. The skill is the highest in the El-Niño region, the Caribbean, the wider Tropics, the Northeast Extra-tropical Pacific and Southwest of the Extra-tropical basins. The skill is not as good for other midlatitudes eastern basins, nor for the Mediterranean, the forecast system being able to represent the low frequency modulation of MHWs but showing poor skill in predicting the interannual variability of the MHW characteristics. Linear trend analysis shows an increase of MHW occurrence at a global scale, which the forecasts capture well. [ABSTRACT FROM AUTHOR]

Marine Heatwaves (MHWs), defined as prolonged period of extremely warm Sea Surface Temperature (SST), have been receiving a lot of attention in the past decade as their frequency and intensity increase in a warming climate. This paper investigates the extent to which the seasonal occurrence and duration of MHWs can be predicted with the European Centre for Medium-range Weather Forecast (ECMWF) operational seasonal forecast system. The prediction of the occurrence of MHW events, the number of MHW days per season, their intensity and spatial extent is derived from SST seasonal forecasts and evaluated against an observation-based SST analysis using both deterministic and probabilistic metrics over the 1982–2021 period. Forecast scores show useful skill in predicting the occurrence of MHWs globally for the two seasons following the starting date. The skill is the highest in the El-Niño region, the Caribbean, the wider Tropics, the Northeast Extra-tropical Pacific and Southwest of the Extra-tropical basins. The skill is not as good for other midlatitudes eastern basins, nor for the Mediterranean, the forecast system being able to represent the low frequency modulation of MHWs but showing poor skill in predicting the interannual variability of the MHW characteristics. Linear trend analysis shows an increase of MHW occurrence at a global scale, which the forecasts capture well. [ABSTRACT FROM AUTHOR]

This study combines operational reforecasts (2001–21) with results from a lower-resolution 41-yr reforecast (1980–2020) to provide a robust assessment of wintertime Euro-Atlantic regimes and their modulation by tropospheric and stratospheric teleconnection pathways in the European Centre for Medium-Range Weather Forecasts (ECMWF) Subseasonal to Seasonal Prediction project (S2S). In both operational and lower-resolution reforecasts, the climatological properties of wintertime Euro-Atlantic regimes, including regime structures, frequencies, and transition probabilities, are accurately simulated at S2S lead times. However, the 41-yr reforecasts allow us to diagnose substantial errors in regime statistics when conditioned on modes of intraseasonal-to-interannual variability. In particular, ECMWF reforecasts underestimate the response of the North Atlantic Oscillation (NAO) to the Madden–Julian oscillation (MJO) and fail to reproduce the modulation of MJO–NAO teleconnections by El Niño–Southern Oscillation (ENSO). Teleconnection and atmospheric wave diagnostics highlight two specific issues that are likely to contribute to these conditional errors in ECMWF reforecasts: (i) insufficient propagation of Rossby wave activity from the Pacific to the Atlantic following MJO phase 3 during El Niño conditions, when the direct tropospheric teleconnection pathway is most active; and (ii) an underestimated response of the stratospheric polar vortex following MJO phase 8 during La Niña conditions, when the indirect stratospheric teleconnection pathway is most active. Improving the representation of tropospheric and stratospheric teleconnection pathways is thus a priority for improving ECMWF forecasts of extratropical weather regimes and their associated surface impacts. Significance Statement: Subseasonal to Seasonal Prediction project (S2S) forecasts are used operationally at ECMWF to provide early warning of cold conditions in Europe associated with persistent large-scale circulation patterns known as weather regimes. On average, ECMWF reforecasts accurately simulate wintertime Euro-Atlantic regime structures and frequencies at S2S lead times. However, regime forecasts show substantial errors when we restrict our analysis to certain phases of intraseasonal and interannual variability, such as El Niño–Southern Oscillation (ENSO). These errors are related to deficiencies in the simulated response of weather regimes to well-predicted variability in the tropics. Improving the representation of such tropical–extratropical teleconnections will improve predictions of extratropical weather regimes and their associated surface impacts. [ABSTRACT FROM AUTHOR]

Satellite altimetry measurements of sea surface height provide near‐global ocean state observations on sub‐monthly time scales, which are not always utilized by seasonal climate forecasting systems. As early as the mid‐1990s, attempts were made to assimilate altimetry observations to initialize climate models. These experiments demonstrated improved ocean forecasting skill, especially compared to experiments that did not assimilate subsurface ocean temperature information. Nowadays, some operational climate forecasting models utilize altimetry in their assimilation systems, whereas others do not. Here, we assess the impact of altimetry assimilation on seasonal prediction skill of ocean variables in two climate forecasting systems that are from the European Centre for Medium‐Range Weather Forecasts (SEAS5) and the Australian Bureau of Meteorology (ACCESS‐S). We show that assimilating altimetry improves the initialization of subsurface ocean temperatures, as well as seasonal forecasts of monthly variability in upper‐ocean heat content and sea level. Skill improvements are largest in the subtropics, where there are typically less subsurface ocean observations available to initialize the forecasts. In the tropics, there are no noticeable improvements in forecast skill. The positive impact of altimetry assimilation on forecast skill related to the subsurface ocean does not seem to affect predictions of sea surface temperature. Whether this is because current forecasting systems are close to the potential predictability limit for the ocean surface, or perhaps altimetry observations are not fully exploited, remains a question. In summary, we find that utilizing altimetry observations improves the overall global ocean forecasting skill, at least for upper‐ocean heat content and sea level. Plain Language Summary: Sea surface heights have been nearly continuously and globally measured by satellite‐based altimeters for almost 30 years, yet not all climate models utilize these observations during their initialization phases of seasonal forecasting. Since the local sea surface height (SSH), or sea level, is mostly determined by the ocean temperature and salinity‐controlled subsurface density structure, the altimetry measurements contain a vast amount of integrated information about the ocean climate state. Climate forecasting systems are usually most skillful when they start from the most accurate initial conditions, including the state of the ocean density structure. Unfortunately, temperature and salinity observations are sparse in many parts of the world's oceans, which degrades the initialization of models and thus possibly diminishes forecast skill. Here, we quantify the benefits of using altimetry observations to initialize two state‐of‐the‐art climate forecasting systems by verifying seasonal forecasts using observational products of upper‐ocean temperature, sea surface temperature, and SSH. In the experiments that did not use altimetry data assimilation, we find an overall decrease of seasonal forecasting skill for the subsurface temperature and SSH, which is especially pronounced in the global subtropics, although we find no widespread change in skill for sea surface temperature. Thus, for predicting the subsurface ocean, climate forecasting systems may benefit from incorporating altimetry data assimilation into their ocean initial conditions. More work is needed to assess how much of the subsurface skill improvements can potentially affect the surface ocean. Key Points: Assimilating observations of sea surface height improves initialization of subsurface ocean temperatures in climate forecasting systemsBy including altimetry assimilation, monthly forecasts are improved of upper‐ocean heat content and sea levelSea surface temperature is only minimally affected by altimetry assimilation, at least in the seasonal forecasts assessed here [ABSTRACT FROM AUTHOR]