Towards Improved Sea Ice Initialization and Forecasting with the IFS
ECMWF Technical Memoranda n. 844 (2019)
Abstract:
The ECMWF analysis system currently assimilates Level-4 sea ice concentration (SIC) from OSTIA (the Operational SST and Sea Ice Analysis produced by the UK Met Office). Here, we evaluate the impact of assimilating Level-3 SIC observations in the ECMWF ocean-sea ice analysis system. Furthermore, we make use of the availability of Arctic-wide sea ice thickness (SIT) observations in the recent years to constrain the modelled sea ice thickness. Coupled forecasts of the ocean-seaice-wave-land-atmosphere are then initialized using the improved sea-ice initial conditions from the above assimilation experiments, and the predictive skill of Arctic sea ice up to lead times of 7 months is investigated in a low-resolution analogue of the currently operational ECMWF seasonal forecasting system SEAS5. Results show that the system successfully assimilates Level-3 SIC observations from the OSISAF (EUMETSAT Ocean and Sea Ice Satellite Applications Facility) product OSI-401-b. Differences in the analysis are small and within the observational uncertainties, but the assimilation of Level-3 SIC will result in increased operational reliability. The impact on coupled forecasts is generally positive for SIC at lead month 1 and neutral for longer lead times. Statistically significant improvements are found over the ice edge and coastal seas in the Arctic mostly in the first 2 weeks for forecasts initialized in most calendar months, except for January starts, when the impact is neutral. The positive impact persists up to week 4 for March, May, August, November and December start months. For SIT and sea ice volume, the forecast impact of Level-3 SIC assimilation is neutral in all lead months.
Using SIT information from CS2-SMOS (CryoSat2-Soil Moisture and Ocean Salinity) as an additional constraint results in substantial changes of sea ice volume and thickness in the ocean-sea ice analysis. Forecasts started from these sea-ice initial conditions show a reduction of the positive sea ice bias and an overall reduction of summer-time forecast errors compared to SEAS5. A slight degradation in skill is found in the autumn sea ice forecasts initialized in July and August. While there is improvement in the skill of autumn 2m-temperature forecast initialized in spring, a degradation in skill is found for the October forecasts initialized in August. We conclude that the strong thinning by CS2-SMOS initialization mitigates or enhances seasonally dependent forecast model errors in sea ice and near surface temperatures. Hence, changes in root-mean-square errors are predominantly due to changes in biases. Using a novel metric, the Integrated Ice Edge Error (IIEE), we find significant improvement of up to 28% in the September sea ice extent forecast started from April. Our results demonstrate the usefulness of new sea ice observational products in both data assimilation and forecast verification, and strongly suggest that better initialization of SIT is crucial for improving seasonal sea-ice forecasts.
Using SIT information from CS2-SMOS (CryoSat2-Soil Moisture and Ocean Salinity) as an additional constraint results in substantial changes of sea ice volume and thickness in the ocean-sea ice analysis. Forecasts started from these sea-ice initial conditions show a reduction of the positive sea ice bias and an overall reduction of summer-time forecast errors compared to SEAS5. A slight degradation in skill is found in the autumn sea ice forecasts initialized in July and August. While there is improvement in the skill of autumn 2m-temperature forecast initialized in spring, a degradation in skill is found for the October forecasts initialized in August. We conclude that the strong thinning by CS2-SMOS initialization mitigates or enhances seasonally dependent forecast model errors in sea ice and near surface temperatures. Hence, changes in root-mean-square errors are predominantly due to changes in biases. Using a novel metric, the Integrated Ice Edge Error (IIEE), we find significant improvement of up to 28% in the September sea ice extent forecast started from April. Our results demonstrate the usefulness of new sea ice observational products in both data assimilation and forecast verification, and strongly suggest that better initialization of SIT is crucial for improving seasonal sea-ice forecasts.
Challenges in Quantifying Changes in the Global Water Cycle
Bulletin of the American Meteorological Society American Meteorological Society 96:7 (2015) 1097-1115
Have greenhouse gases intensified the contrast between wet and dry regions?
Geophysical Research Letters American Geophysical Union (AGU) 40:17 (2013) 4783-4787
Fingerprints of changes in annual and seasonal precipitation from CMIP5 models over land and ocean
Geophysical Research Letters American Geophysical Union (AGU) 39:21 (2012)
High frequency variability of the Atlantic meridional overturning circulation
Ocean Science 7 (2011) 471–486
Abstract:
We compare the variability of the Atlantic meridional overturning circulation (AMOC) as simulated by the coupled climate models of the RAPID project, which cover a wide range of resolution and complexity, and observed by the RAPID/MOCHA array at about 26° N. We analyse variability on a range of timescales, from five-daily to interannual. In models of all resolutions there is substantial variability on timescales of a few days; in most AOGCMs the amplitude of the variability is of somewhat larger magnitude than that observed by the RAPID array, while the time-mean is within about 10 % of the observational estimate. The amplitude of the simulated annual cycle is similar to observations, but the shape of the annual cycle shows a spread among the models. A dynamical decomposition shows that in the models, as in observations, the AMOC is predominantly geostrophic (driven by pressure and sea-level gradients), with both geostrophic and Ekman contributions to variability, the latter being exaggerated and the former underrepresented in models. Other ageostrophic terms, neglected in the observational estimate, are small but not negligible. The time-mean of the western boundary current near the latitude of the RAPID/MOCHA array has a much wider model spread than the AMOC does, indicating large differences among models in the simulation of the wind-driven gyre circulation, and its variability is unrealistically small in the models. In many RAPID models and in models of the Coupled Model Intercomparison Project Phase 3 (CMIP3), interannual variability of the maximum of the AMOC wherever it lies, which is a commonly used model index, is similar to interannual variability in the AMOC at 26° N. Annual volume and heat transport timeseries at the same latitude are well-correlated within 15–45° N, indicating the climatic importance of the AMOC. In the RAPID and CMIP3 models, we show that the AMOC is correlated over considerable distances in latitude, but not the whole extent of the North Atlantic; consequently interannual variability of the AMOC at 50° N, where it is particularly relevant to European climate, is not well-correlated with that of the AMOC at 26° N, where it is monitored by the RAPID/MOCHA array.