scholarly journals Impact of Ocean Observation Systems on Ocean Analysis and Seasonal Forecasts

2007 ◽  
Vol 135 (2) ◽  
pp. 409-429 ◽  
Author(s):  
A. Vidard ◽  
D. L. T. Anderson ◽  
M. Balmaseda
Keyword(s):  
Data Assimilation ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Tropical Atlantic ◽  
Seasonal Forecasts ◽  
The North ◽  
Ocean Data Assimilation ◽  
Observation Systems ◽  
The Impact

Abstract The relative merits of the Tropical Atmosphere–Ocean (TAO)/Triangle Trans-Ocean Buoy Network (TAO/TRITON) and Pilot Research Moored Array in the Tropical Atlantic mooring networks, the Voluntary Observing Ship (VOS) expendable bathythermograph (XBT) network, and the Argo float network are evaluated through their impact on ocean analyses and seasonal forecast skill. An ocean analysis is performed in which all available data are assimilated. In two additional experiments the moorings and the VOS datasets are withheld from the assimilation. To estimate the impact on seasonal forecast skill, the set of ocean analyses is then used to initialize a corresponding set of coupled ocean–atmosphere model forecasts. A further set of experiments is conducted to assess the impact of the more recent Argo array. A key parameter for seasonal forecast initialization is the depth of the thermocline in the tropical Pacific. This depth is quite similar in all of the experiments that involve data assimilation, but withdrawing the TAO data has a bigger effect than withdrawing XBT data, especially in the eastern half of the basin. The forecasts mainly indicate that the TAO/TRITON in situ temperature observations are essential to obtain optimum forecast skill. They are best combined with XBT, however, because this results in better predictions for the west Pacific. Furthermore, the XBTs play an important role in the North Atlantic. The ocean data assimilation performs less well in the tropical Atlantic. This may be partly a result of not having adequate observations of salinity.

scholarly journals How essential are Argo observations to constrain a global ocean data assimilation system?

2015 ◽  
Vol 12 (3) ◽  
pp. 1145-1186 ◽  
Author(s):  
V. Turpin ◽  
E. Remy ◽  
P. Y. Le Traon
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Global Ocean ◽  
Altimeter Data ◽  
Western Boundary ◽  
Argo Data ◽  
Ocean Data Assimilation ◽  
In Situ Observations ◽  
The Impact

Abstract. Observing System Experiments (OSEs) are carried out over a one-year period to quantify the impact of Argo observations on the Mercator-Ocean 1/4° global ocean analysis and forecasting system. The reference simulation assimilates sea surface temperature (SST), SSALTO/DUACS altimeter data and Argo and other in situ observations from the Coriolis data center. Two other simulations are carried out where all Argo and half of Argo data sets are withheld. Assimilating Argo observations has a significant impact on analyzed and forecast temperature and salinity fields at different depths. Without Argo data assimilation, large errors occur in analyzed fields as estimated from the differences when compared with in situ observations. For example, in the 0–300 m layer RMS differences between analyzed fields and observations reach 0.25 psu and 1.25 °C in the western boundary currents and 0.1 psu and 0.75 °C in the open ocean. The impact of the Argo data in reducing observation-model forecast error is also significant from the surface down to a depth of 2000 m. Differences between independent observations and forecast fields are thus reduced by 20 % in the upper layers and by up to 40 % at a depth of 2000 m when Argo data are assimilated. At depth, the most impacted regions in the global ocean are the Mediterranean outflow and the Labrador Sea. A significant degradation can be observed when only half of the data are assimilated. All Argo observations thus matter, even with a 1/4° model resolution. The main conclusion is that the performance of global data assimilation systems is heavily dependent on the availability of Argo data.

Biases in the Tropical Atlantic in Seasonal Forecast System GloSea5

2021 ◽  
Author(s):  
Tamara Collier ◽  
Jamie Kettleborough ◽  
Adam Scaife ◽  
Leon Hermanson ◽  
Philip Davis
Keyword(s):  
North Atlantic ◽  
Climate Model ◽  
Seasonal Forecast ◽  
Boreal Summer ◽  
Tropical Atlantic ◽  
Cold Tongue ◽  
Forecast System ◽  
The North ◽  
The North Atlantic ◽  
The Impact

<p>It is well known that climate models commonly show biases in the Tropical Atlantic including reduced cold tongue development in the boreal summer. This work investigates whether these biases are present in the Met Office Seasonal Forecast System (GloSea5) at seasonal lead times and the impact they have on teleconnections to the North Atlantic, a key area for forecasting for Northern Europe.</p><p>GloSea5 hindcasts covering the period 1993 – 2016 are analysed for winter and summer start dates and biases are calculated with comparison to ERA Interim for sea surface temperature, near surface winds and upper tropospheric winds, and the Global Precipitation Climatology Project (GPCP) for Rainfall Rate. In contrast to fully developed climate model biases, enhanced cold tongue development is found in the summer months, and a general cold bias occurs in the SST in both winter and summer. This shows that biases in initialised forecasts do not simply asymptote to the climate model error but show more complex behaviour including a change in the sign of the bias. Easterly winds are found to be strengthened throughout and signs of a double Inter Tropical Convergence Zone (ITCZ) are observed in the winter season. The ITCZ in both seasons is shown to be a narrower band of heavier rain in GloSea5 compared to the GPCP.  We investigate how these tropical biases propagate into the North Atlantic and change the forecast biases there.</p>

scholarly journals Assimilating compact phase space retrievals (CPSRs): comparison with independent observations (MOZAIC in situ and IASI retrievals) and extension to assimilation of truncated retrieval profiles

2018 ◽  
Vol 11 (9) ◽  
pp. 3727-3745 ◽  
Author(s):  
Arthur P. Mizzi ◽  
David P. Edwards ◽  
Jeffrey L. Anderson
Keyword(s):  
Carbon Monoxide ◽  
Phase Space ◽  
Data Assimilation ◽  
Computational Cost ◽  
Lower Troposphere ◽  
Forecast Skill ◽  
Promising Alternative ◽  
Independent Observations ◽  
The Impact

Abstract. Assimilation of atmospheric composition retrievals presents computational challenges due to their high data volume and often sparse information density. Assimilation of compact phase space retrievals (CPSRs) meets those challenges and offers a promising alternative to assimilation of raw retrievals at reduced computational cost (Mizzi et al., 2016). This paper compares analysis and forecast results from assimilation of Terra/Measurement of Pollution in the Troposphere (MOPITT) carbon monoxide (CO) CPSRs with independent observations. We use MetOp-A/Infrared Atmospheric Sounding Interferometer (IASI) CO retrievals and Measurement of OZone, water vapor, carbon monoxide, and nitrogen oxides by in-service AIrbus airCraft (MOZAIC) in situ CO profiles for our independent observation comparisons. Generally, the results confirm that assimilation of MOPITT CPSRs improves the Weather Research and Forecasting model with chemistry coupled to the ensemble Kalman filter data assimilation from the Data Assimilation Research Testbed (WRF-Chem/DART) analysis fit and forecast skill at a reduced computational cost compared to assimilation of raw retrievals. Comparison with the independent observations shows that assimilation of MOPITT CO generally improved the analysis fit and forecast skill in the lower troposphere but degraded it in the upper troposphere. We attribute that degradation to assimilation of MOPITT CO retrievals with a possible bias of  ∼ 14 % above 300 hPa. To discard the biased retrievals, in this paper, we also extend CPSRs to assimilation of truncated retrieval profiles (as opposed to assimilation of full retrieval profiles). Those results show that not assimilating the biased retrievals (i) resolves the upper tropospheric analysis fit degradation issue and (ii) reduces the impact of assimilating the remaining unbiased retrievals because the total information content and vertical sensitivities are changed.

scholarly journals Yearly evolution of Euro-Atlantic weather regimes and of their sub-seasonal predictability

2021 ◽  
Author(s):  
Nicola Cortesi ◽  
Verónica Torralba ◽  
Llorenó Lledó ◽  
Andrea Manrique-Suñén ◽  
Nube Gonzalez-Reviriego ◽  
...  
Keyword(s):  
Annual Cycle ◽  
Winter Season ◽  
Skill Score ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Sea Level Pressure ◽  
Weather Regimes ◽  
Start Dates ◽  
Weekly Frequency

AbstractIt is often assumed that weather regimes adequately characterize atmospheric circulation variability. However, regime classifications spanning many months and with a low number of regimes may not satisfy this assumption. The first aim of this study is to test such hypothesis for the Euro-Atlantic region. The second one is to extend the assessment of sub-seasonal forecast skill in predicting the frequencies of occurrence of the regimes beyond the winter season. Two regime classifications of four regimes each were obtained from sea level pressure anomalies clustered from October to March and from April to September respectively. Their spatial patterns were compared with those representing the annual cycle. Results highlight that the two regime classifications are able to reproduce most part of the patterns of the annual cycle, except during the transition weeks between the two periods, when patterns of the annual cycle resembling Atlantic Low regime are not also observed in any of the two classifications. Forecast skill of Atlantic Low was found to be similar to that of NAO+, the regime replacing Atlantic Low in the two classifications. Thus, although clustering yearly circulation data in two periods of 6 months each introduces a few deviations from the annual cycle of the regime patterns, it does not negatively affect sub-seasonal forecast skill. Beyond the winter season and the first ten forecast days, sub-seasonal forecasts of ECMWF are still able to achieve weekly frequency correlations of r = 0.5 for some regimes and start dates, including summer ones. ECMWF forecasts beat climatological forecasts in case of long-lasting regime events, and when measured by the fair continuous ranked probability skill score, but not when measured by the Brier skill score. Thus, more efforts have to be done yet in order to achieve minimum skill necessary to develop forecast products based on weather regimes outside winter season.

Impact of mesoscale eddies on salinity and CO2 ocean parameters in the western tropical Atlantic in February 2020

2021 ◽  
Author(s):  
Léa Olivier ◽  
Jacqueline Boutin ◽  
Nathalie Lefèvre ◽  
Gilles Reverdin ◽  
Peter Landschützer ◽  
...  
Keyword(s):  
Dissolved Inorganic Carbon ◽  
Salinity Gradient ◽  
Sea Surface Salinity ◽  
Amazon River ◽  
Tropical Atlantic ◽  
Surface Salinity ◽  
The North ◽  
Brazil Current ◽  
North Brazil

<p>Large oceanic eddies are formed by the retroflection of the North Brazil Current (NBC) near 8°N in the western tropical Atlantic. The EUREC<sup>4</sup>A-OA/Atomic cruise took place in January - February 2020, and extensively documented two NBC rings. The NBC flows northward across the Equator and pass the mouth of the Amazon River, entraining fresh and nutrient-rich water along its nearshore edge. From December to March, the Amazon river discharge is low but a freshwater filament stirred by a NBC ring was nevertheless observed. The strong salinity gradient can be used to delineate the NBC ring during its initial phase and its westward propagation. Using satellite sea surface salinity and ocean color associated to in-situ measurements of salinity, temperature, dissolved inorganic carbon, alkalinity and fugacity of CO<sub>2</sub> we characterize the salinity and biogeochemical signature of NBC rings.</p>

scholarly journals Seasonal Forecasts of the Twentieth Century

2020 ◽  
Vol 101 (8) ◽  
pp. E1413-E1426 ◽  
Author(s):  
Antje Weisheimer ◽  
Daniel J. Befort ◽  
Dave MacLeod ◽  
Tim Palmer ◽  
Chris O’Reilly ◽  
...  
Keyword(s):  
Twentieth Century ◽  
Large Scale ◽  
Seasonal Forecast ◽  
Forecast Skill ◽  
Seasonal Forecasts ◽  
Climate Anomalies ◽  
Global Circulation Models ◽  
Skill Scores ◽  
Physically Based ◽  
Forecast Models

Abstract Forecasts of seasonal climate anomalies using physically based global circulation models are routinely made at operational meteorological centers around the world. A crucial component of any seasonal forecast system is the set of retrospective forecasts, or hindcasts, from past years that are used to estimate skill and to calibrate the forecasts. Hindcasts are usually produced over a period of around 20–30 years. However, recent studies have demonstrated that seasonal forecast skill can undergo pronounced multidecadal variations. These results imply that relatively short hindcasts are not adequate for reliably testing seasonal forecasts and that small hindcast sample sizes can potentially lead to skill estimates that are not robust. Here we present new and unprecedented 110-year-long coupled hindcasts of the next season over the period 1901–2010. Their performance for the recent period is in good agreement with those of operational forecast models. While skill for ENSO is very high during recent decades, it is markedly reduced during the 1930s–1950s. Skill at the beginning of the twentieth century is, however, as high as for recent high-skill periods. Consistent with findings in atmosphere-only hindcasts, a midcentury drop in forecast skill is found for a range of atmospheric fields, including large-scale indices such as the NAO and the PNA patterns. As with ENSO, skill scores for these indices recover in the early twentieth century, suggesting that the midcentury drop in skill is not due to a lack of good observational data. A public dissemination platform for our hindcast data is available, and we invite the scientific community to explore them.

scholarly journals NO<sub>2</sub> seasonal evolution in the North Subtropical free troposphere

2015 ◽  
Vol 15 (10) ◽  
pp. 14473-14504
Author(s):  
M. Gil-Ojeda ◽  
M. Navarro-Comas ◽  
L. Gómez-Martín ◽  
J. A. Adame ◽  
A. Saiz-Lopez ◽  
...  
Keyword(s):  
Optical Absorption Spectroscopy ◽  
High Mountain ◽  
Horizontal Line ◽  
Free Troposphere ◽  
Seasonal Evolution ◽  
The North ◽  
Measuring Period ◽  
The Impact ◽  
Pollution Events

Abstract. Three years of Multi-Axis Differential Optical Absorption Spectroscopy (MAXDOAS) measurements (2011–2013) have been used for estimating the NO2 mixing ratio along a horizontal line of sight from the high mountain Subtropical observatory of Izaña, at 2370 m a.s.l. (NDACC station, 28.3° N, 16.5° W). The method is based on horizontal path calculation from the O2–O2 collisional complex at the 477 nm absorption band which is measured simultaneously to the NO2, and is applicable under low aerosols loading conditions. The MAXDOAS technique, applied in horizontal mode in the free troposphere, minimizes the impact of the NO2 contamination resulting from the arrival of MBL airmasses from thermally forced upwelling breeze during central hours of the day. Comparisons with in-situ observations show that during most of measuring period the MAXDOAS is insensitive or very little sensitive to the upwelling breeze. Exceptions are found during pollution events under southern wind conditions. On these occasions, evidence of fast efficient and irreversible transport from the surface to the free troposphere is found. Background NO2 vmr, representative of the remote free troposphere, are in the range of 20–45 pptv. The observed seasonal evolution shows an annual wave where the peak is in phase with the solar radiation. Model simulations with the chemistry-climate CAM-Chem model are in good agreement with the NO2 measurements, and are used to further investigate the possible drivers of the NO2 seasonality observed at Izaña.

scholarly journals SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

2019 ◽  
Author(s):  
Nemesio Rodriguez-Fernandez ◽  
Patricia de Rosnay ◽  
Clement Albergel ◽  
Philippe Richaume ◽  
Filipe Aires ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Model ◽  
Northern Asia ◽  
The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

scholarly journals Tropical and Midlatitude Impact on Seasonal Polar Predictability in the Community Earth System Model

2019 ◽  
Vol 32 (18) ◽  
pp. 5997-6014 ◽  
Author(s):  
Edward Blanchard-Wrigglesworth ◽  
Qinghua Ding
Keyword(s):  
Forecast Skill ◽  
Specific Humidity ◽  
The Arctic ◽  
The North ◽  
Perfect Model ◽  
Community Earth System Model ◽  
Free Running ◽  
Skill Improvement ◽  
The Tropics ◽  
The Impact

Abstract The impact on seasonal polar predictability from improved tropical and midlatitude forecasts is explored using a perfect-model experiment and applying a nudging approach in a GCM. We run three sets of 7-month long forecasts: a standard free-running forecast and two nudged forecasts in which atmospheric winds, temperature, and specific humidity (U, V, T, Q) are nudged toward one of the forecast runs from the free ensemble. The two nudged forecasts apply the nudging over different domains: the tropics (30°S–30°N) and the tropics and midlatitudes (55°S–55°N). We find that the tropics have modest impact on forecast skill in the Arctic or Antarctica both for sea ice and the atmosphere that is mainly confined to the North Pacific and Bellingshausen–Amundsen–Ross Seas, whereas the midlatitudes greatly improve Arctic winter and Antarctic year-round forecast skill. Arctic summer forecast skill from May initialization is not strongly improved in the nudged forecasts relative to the free forecast and is thus mostly a “local” problem. In the atmosphere, forecast skill improvement from midlatitude nudging tends to be largest in the polar stratospheres and decreases toward the surface.

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