Effects of fluctuating daily surface fluxes on the time-mean oceanic circulation

The effect of fluctuating daily surface fluxes on the time-mean oceanic circulation is studied using an empirical flux model. The model produces fluctuating fluxes resulting from atmospheric variability and includes oceanic feedbacks on the fluxes. Numerical experiments were carried out by driving an ocean general circulation model with three different versions of the empirical model. It is found that fluctuating daily fluxes lead to an increase in the meridional overturning circulation (MOC) of the Atlantic of about 1 Sv and a decrease in the Antarctic circumpolar current (ACC) of about 32 Sv. The changes are approximately 7% of the MOC and 16% of the ACC obtained without fluctuating daily fluxes. The fluctuating fluxes change the intensity and the depth of vertical mixing. This, in turn, changes the density field and thus the circulation. Fluctuating buoyancy fluxes change the vertical mixing in a non-linear way: they tend to increase the convective mixing in mostly stable regions and to decrease the convective mixing in mostly unstable regions. The ACC changes are related to the enhanced mixing in the subtropical and the mid-latitude Southern Ocean and reduced mixing in the high-latitude Southern Ocean. The enhanced mixing is related to an increase in the frequency and the depth of convective events. As these events bring more dense water downward, the mixing changes lead to a reduction in meridional gradient of the depth-integrated density in the Southern Ocean and hence the strength of the ACC. The MOC changes are related to more subtle density changes. It is found that the vertical mixing in a latitudinal strip in the northern North Atlantic is more strongly enhanced due to fluctuating fluxes than the mixing in a latitudinal strip in the South Atlantic. This leads to an increase in the density difference between the two strips, which can be responsible for the increase in the Atlantic MOC

Fingerprints of changes in annual and seasonal precipitation from CMIP5 models over land and ocean

By comparing annual and seasonal changes in precipitation over land and ocean since 1950 simulated by the CMIP5 (Coupled Model Intercomparison Project, phase 5) climate models in which natural and anthropogenic forcings have been included, we find that clear global-scale and regional-scale changes due to human influence are expected to have occurred over both land and ocean. These include moistening over northern high latitude land and ocean throughout all seasons and over the northern subtropical oceans during boreal winter. However we show that this signal of human influence is less distinct when considered over the relatively small area of land for which there are adequate observations to make assessments of multi-decadal scale trends. These results imply that extensive and significant changes in precipitation over the land and ocean may have already happened, even though, inadequacies in observations in some parts of the world make it difficult to identify conclusively such a human fingerprint on the global water cycle. In some regions and seasons, due to aliasing of different kinds of variability as a result of sub sampling by the sparse and changing observational coverage, observed trends appear to have been increased, underscoring the difficulties of interpreting the apparent magnitude of observed changes in precipitation

Impact of ocean in-situ observations on ECMWF sub-seasonal forecasts

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

Impact of the ocean in-situ observations on the ECMWF seasonal forecasting system

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

Assessment of AtlantOS impact

Assessment of the impact of AtlantOS in situ observing system for Copernicus Marine Service and seasonal predictio

Seasonal Arctic sea ice forecasting with probabilistic deep learning.

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss

Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU’s Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services.</jats:p

Challenges in quantifying changes in the global water cycle

Human influences have likely already impacted the large-scale water cycle but natural variability and observational uncertainty are substantial. It is essential to maintain and improve observational capabilities to better characterize changes. Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time-series over land but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols, and due to large climate variability presently limits confidence in attribution of observed changes

Assessing the Impact of Ocean In Situ Observations on MJO Propagation Across the Maritime Continent in ECMWF Subseasonal Forecasts

Abstract Despite the well‐recognized initial value nature of the subseasonal forecasts, the role of subsurface ocean initialization in subseasonal forecasts remains underexplored. Using observing system experiments, this study investigates the impact of ocean in situ data assimilation on the propagation of Madden–Julian Oscillation (MJO) events across the Maritime Continent in the European Centre for Medium‐Range Weather Forecasts (ECMWF) subseasonal forecast system. Two sets of twin experiments are analyzed, which only differ on the use or not of in situ ocean observations in the initial conditions. Besides using the Real‐time Multivariate MJO Index (RMMI) to evaluate the forecast performance, we also develop a new MJO tracking method based on outgoing longwave radiation anomalies (OLRa) for forecast evaluation. We find that the ocean initialization with in situ data assimilation, though having an impact on the forecasted ocean mean state, does not improve the relatively low MJO forecast skill across the Maritime Continent. Moist static energy budget analysis further suggests that a significant underestimation in the meridional moisture advection in the model forecast may hinder the potential role played by the ocean state differences associated with data assimilation. Bias of the intraseasonal meridional winds in the model is a more important factor for such underestimation than the mean state moisture biases. This finding suggests that atmospheric model biases dominate the forecast error growth, and the atmospheric circulation bias is one of the major sources of the MJO prediction error and should be a target for improving the ECMWF subseasonal forecast model

Data used in "Assessing the Impact of Ocean In-situ Data Assimilation on MJO Propagation across the Maritime Continent in ECMWF Subseasonal Forecasts"

Despite the well-recognized initial value nature of the subseasonal forecasts, the role of subsurface ocean initialization in subseasonal forecasts remains underexplored. Using observing system experiments, this study investigates the impact of ocean in-situ data assimilation on the propagation of Madden--Julian Oscillation (MJO) across the Maritime Continent in the European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal forecast system. Two sets of twin experiments are analyzed, which only differ on the use or not of in-situ ocean observations in the initial conditions. Besides using the Real-time Multivariate MJO Index (RMMI) to evaluate the forecast performance, we also develop a new MJO tracking method based on outgoing longwave radiation anomalies (OLRa) for forecast evaluation. We find that the ocean initialization with in-situ data assimilation, though having an impact on the forecasted ocean mean state, does not improve the relatively low MJO forecast skill across the Maritime Continent. Moist Static Energy budget analysis further suggests that a significant underestimation in the meridional moisture advection in the model forecast hinders the potential role played by the ocean state differences associated with data assimilation. Bias of the intraseasonal meridional winds in the model is a more important factor for such underestimation than the mean state moisture biases. This finding suggests that the atmospheric model biases dominate the forecast error growth, and the atmospheric circulation bias is one of the major sources of the MJO prediction error and should be a target for improving the ECMWF subseasonal forecast model.</p