Prospects for seasonal forecasting of iceberg distributions in the North Atlantic

An efficient approach to ocean–iceberg modelling provides a means for assessing prospects for seasonal forecasting of iceberg distributions in the northwest Atlantic, where icebergs present a hazard to mariners each spring. The stand-alone surface (SAS) module that is part of the Nucleus for European Modelling of the Ocean (NEMO) is coupled with the NEMO iceberg module (ICB) in a “SAS-ICB” configuration with horizontal resolution of 0.25°. Iceberg conditions are investigated for three recent years, 2013–2015, characterized by widely varying iceberg distributions. The relative simplicity of SAS-ICB facilitates efficient investigation of sensitivity to iceberg fluxes and prevailing environmental conditions. SAS-ICB is provided with daily surface ocean analysis fields from the global Forecasting Ocean Assimilation Model (FOAM) of the Met Office. Surface currents, temperatures and height together determine iceberg advection and melting rates. Iceberg drift is further governed by surface winds, which are updated every 3 h. The flux of icebergs from the Greenland ice sheet is determined from engineering control theory and specified as an upstream flux in the vicinity of Davis Strait for January or February. Simulated iceberg distributions are evaluated alongside observations reported and archived by the International Ice Patrol. The best agreement with observations is obtained when variability in both upstream iceberg flux and oceanographic/atmospheric conditions is taken into account. Including interactive icebergs in an ocean–atmosphere model with sufficient seasonal forecast skill, and provided with accurate winter iceberg fluxes, it is concluded that seasonal forecasts of spring/summer iceberg conditions for the northwest Atlantic are now a realistic prospect

An assessment of air-sea heat fluxes from ocean and coupled reanalyses

Sixteen monthly air–sea heat flux products from global ocean/coupled reanalyses are compared over 1993–2009 as part of the Ocean Reanalysis Intercomparison Project (ORA-IP). Objectives include assessing the global heat closure, the consistency of temporal variability, comparison with other flux products, and documenting errors against in situ flux measurements at a number of OceanSITES moorings. The ensemble of 16 ORA-IP flux estimates has a global positive bias over 1993–2009 of 4.2 ± 1.1 W m−2. Residual heat gain (i.e., surface flux + assimilation increments) is reduced to a small positive imbalance (typically, +1–2 W m−2). This compensation between surface fluxes and assimilation increments is concentrated in the upper 100 m. Implied steady meridional heat transports also improve by including assimilation sources, except near the equator. The ensemble spread in surface heat fluxes is dominated by turbulent fluxes (>40 W m−2 over the western boundary currents). The mean seasonal cycle is highly consistent, with variability between products mostly <10 W m−2. The interannual variability has consistent signal-to-noise ratio (~2) throughout the equatorial Pacific, reflecting ENSO variability. Comparisons at tropical buoy sites (10°S–15°N) over 2007–2009 showed too little ocean heat gain (i.e., flux into the ocean) in ORA-IP (up to 1/3 smaller than buoy measurements) primarily due to latent heat flux errors in ORA-IP. Comparisons with the Stratus buoy (20°S, 85°W) over a longer period, 2001–2009, also show the ORA-IP ensemble has 16 W m−2 smaller net heat gain, nearly all of which is due to too much latent cooling caused by differences in surface winds imposed in ORA-IP

Indian summer monsoon onset forecast skill in the UK Met Office initialized coupled seasonal forecasting system (GloSea5-GC2)

Accurate and precise forecasting of the Indian monsoon is important for the socio-economic security of India, with improvements in agriculture and associated sectors from prediction of the monsoon onset. In this study we establish the skill of the UK Met Office coupled initialized global seasonal forecasting system, GloSea5-GC2, in forecasting Indian monsoon onset. We build on previous work that has demonstrated the good skill of GloSea5 at forecasting interannual variations of the seasonal mean Indian monsoon using measures of large-scale circulation and local precipitation. We analyze the summer hindcasts from a set of three springtime start-dates in late April/early May for the 20-year hindcast period (1992-2011). The hindcast set features at least fifteen ensemble members for each year and is analyzed using five different objective monsoon indices. These indices are designed to examine large and local-scale measures of the monsoon circulation, hydrological changes, tropospheric temperature gradient, or rainfall for single value (area-averaged) or grid-point measures of the Indian monsoon onset. There is significant correlation between onset dates in the model and those found in reanalysis. Indices based on large-scale dynamic and thermodynamic indices are better at estimating monsoon onset in the model rather than local-scale dynamical and hydrological indices. This can be attributed to the model's better representation of large-scale dynamics compared to local-scale features. GloSea5 may not be able to predict the exact date of monsoon onset over India, but this study shows that the model has a good ability at predicting category-wise monsoon onset, using early, normal or late tercile categories. Using a grid-point local rainfall onset index, we note that the forecast skill is highest over parts of central India, the Gangetic plains, and parts of coastal India - all zones of extensive agriculture in India. El Niño Southern Oscillation (ENSO) forcing in the model improves the forecast skill of monsoon onset when using a large-scale circulation index, with late monsoon onset coinciding with El Niño conditions and early monsoon onset more common in La Niña years. The results of this study suggest that GloSea5's ensemble-mean forecast may be used for reliable Indian monsoon onset prediction a month in advance despite systematic model errors

Steric sea level variability (1993-2010) in an ensemble of ocean reanalyses and objective analyses

Quantifying the effect of the seawater density changes on sea level variability is of crucial importance for climate change studies, as the sea level cumulative rise can be regarded as both an important climate change indicator and a possible danger for human activities in coastal areas. In this work, as part of the Ocean Reanalysis Intercomparison Project, the global and regional steric sea level changes are estimated and compared from an ensemble of 16 ocean reanalyses and 4 objective analyses. These estimates are initially compared with a satellite-derived (altimetry minus gravimetry) dataset for a short period (2003–2010). The ensemble mean exhibits a significant high correlation at both global and regional scale, and the ensemble of ocean reanalyses outperforms that of objective analyses, in particular in the Southern Ocean. The reanalysis ensemble mean thus represents a valuable tool for further analyses, although large uncertainties remain for the inter-annual trends. Within the extended intercomparison period that spans the altimetry era (1993–2010), we find that the ensemble of reanalyses and objective analyses are in good agreement, and both detect a trend of the global steric sea level of 1.0 and 1.1 ± 0.05 mm/year, respectively. However, the spread among the products of the halosteric component trend exceeds the mean trend itself, questioning the reliability of its estimate. This is related to the scarcity of salinity observations before the Argo era. Furthermore, the impact of deep ocean layers is non-negligible on the steric sea level variability (22 and 12 % for the layers below 700 and 1500 m of depth, respectively), although the small deep ocean trends are not significant with respect to the products spread

The Met Office Global Coupled model 3.0 and 3.1 (GC3.0 &amp; GC3.1) configurations

The Global Coupled 3 (GC3) configuration of the Met Office Unified Model is presented. Amongst other applications, GC3 is the basis of the United Kingdom's submission to the Coupled Model Intercomparison Project 6 (CMIP6). This paper documents the model components that make up the configuration (although the scientific description of these components are in companion papers), and details the coupling between them. The performance of GC3 is assessed in terms of mean biases and variability in long climate simulations using present-day forcing. The suitability of the configuration for predictabiity on shorter timescales (weather and seasonal forecasting) is also briefly discussed. The performance of GC3 is compared against GC2, the previous Met Office coupled model configuration, and against an older configuration (HadGEM2-AO) which was the submission to CMIP5. In many respects, the performance of GC3 is comparable with GC2, however there is a notable improvement in the Southern Ocean warm sea surface temperature bias which has been reduced by 75%, and there are improvements in cloud amount and some aspects of tropical variability. Relative to HadGEM2-AO, many aspects of the present-day climate are improved in GC3 including tropospheric and stratospheric temperature structure, most aspects of tropical and extra-tropical variability and top-of-atmosphere &amp; surface fluxes. A number of outstanding errors are identified including a residual asymmetric sea surface temperature bias (cool northern hemisphere, warm Southern Ocean), an overly strong global hydrological cycle and insufficient European blocking.</p