Multi-model assessment of the impact of soil moisture initialization on mid-latitude summer predictability

Land surface initial conditions have been recognized as a potential source of predictability in sub-seasonal to seasonal forecast systems, at least for near-surface air temperature prediction over the mid-latitude continents. Yet, few studies have systematically explored such an influence over a sufficient hindcast period and in a multi-model framework to produce a robust quantitative assessment. Here, a dedicated set of twin experiments has been carried out with boreal summer retrospective forecasts over the 1992–2010 period performed by five different global coupled ocean–atmosphere models. The impact of a realistic versus climatological soil moisture initialization is assessed in two regions with high potential previously identified as hotspots of land–atmosphere coupling, namely the North American Great Plains and South-Eastern Europe. Over the latter region, temperature predictions show a significant improvement, especially over the Balkans. Forecast systems better simulate the warmest summers if they follow pronounced dry initial anomalies. It is hypothesized that models manage to capture a positive feedback between high temperature and low soil moisture content prone to dominate over other processes during the warmest summers in this region. Over the Great Plains, however, improving the soil moisture initialization does not lead to any robust gain of forecast quality for near-surface temperature. It is suggested that models biases prevent the forecast systems from making the most of the improved initial conditions.The authors thank Jeff Knight (Met Office Hadley Centre) for his constructive comments on earlier versions of this manuscript. The research leading to these results received funding from the European Union Seventh Framework Programme (FP7/2007–2013) SPECS project (Grant Agreement Number 308378) and H2020 Framework Programme IMPREX project (Grant Agreement Number 641811). Constantin Ardilouze was also supported by the BSC Centro de Excelencia Severo Ochoa Programme.Peer ReviewedPostprint (author's final draft

How to create an operational multi-model of seasonal forecasts?

Seasonal forecasts of variables like near-surface temperature or precipitation are becoming increasingly important for a wide range of stakeholders. Due to the many possibilities of recalibrating, combining, and verifying ensemble forecasts, there are ambiguities of which methods are most suitable. To address this we compare approaches how to process and verify multi-model seasonal forecasts based on a scientific assessment performed within the framework of the EU Copernicus Climate Change Service (C3S) Quality Assurance for Multi-model Seasonal Forecast Products (QA4Seas) contract C3S 51 lot 3. Our results underpin the importance of processing raw ensemble forecasts differently depending on the final forecast product needed. While ensemble forecasts benefit a lot from bias correction using climate conserving recalibration, this is not the case for the intrinsically bias adjusted multi-category probability forecasts. The same applies for multi-model combination. In this paper, we apply simple, but effective, approaches for multi-model combination of both forecast formats. Further, based on existing literature we recommend to use proper scoring rules like a sample version of the continuous ranked probability score and the ranked probability score for the verification of ensemble forecasts and multi-category probability forecasts, respectively. For a detailed global visualization of calibration as well as bias and dispersion errors, using the Chi-square decomposition of rank histograms proved to be appropriate for the analysis performed within QA4Seas.The research leading to these results is part of the Copernicus Climate Change Service (C3S) (Framework Agreement number C3S_51_Lot3_BSC), a program being implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission. Francisco Doblas-Reyes acknowledges the support by the H2020 EUCP project (GA 776613) and the MINECO-funded CLINSA project (CGL2017-85791-R)

Randomly correcting model errors in the ARPEGE-Climate v6.1 component of CNRM-CM: applications for seasonal forecasts

Stochastic methods are increasingly used in global coupled model climate forecasting systems to account for model uncertainties. In this paper, we describe in more detail the stochastic dynamics technique introduced by Batté and Déqué (2012) in the ARPEGE-Climate atmospheric model. We present new results with an updated version of CNRM-CM using ARPEGE-Climate v6.1, and show that the technique can be used both as a means of analyzing model error statistics and accounting for model inadequacies in a seasonal forecasting framework.<br><br>The perturbations are designed as corrections of model drift errors estimated from a preliminary weakly nudged re-forecast run over an extended reference period of 34 boreal winter seasons. A detailed statistical analysis of these corrections is provided, and shows that they are mainly made of intra-month variance, thereby justifying their use as in-run perturbations of the model in seasonal forecasts. However, the interannual and systematic error correction terms cannot be neglected. Time correlation of the errors is limited, but some consistency is found between the errors of up to 3 consecutive days.<br><br>These findings encourage us to test several settings of the random draws of perturbations in seasonal forecast mode. Perturbations are drawn randomly but consistently for all three prognostic variables perturbed. We explore the impact of using monthly mean perturbations throughout a given forecast month in a first ensemble re-forecast (SMM, for stochastic monthly means), and test the use of 5-day sequences of perturbations in a second ensemble re-forecast (S5D, for stochastic 5-day sequences). Both experiments are compared in the light of a REF reference ensemble with initial perturbations only. Results in terms of forecast quality are contrasted depending on the region and variable of interest, but very few areas exhibit a clear degradation of forecasting skill with the introduction of stochastic dynamics. We highlight some positive impacts of the method, mainly on Northern Hemisphere extra-tropics. The 500 hPa geopotential height bias is reduced, and improvements project onto the representation of North Atlantic weather regimes. A modest impact on ensemble spread is found over most regions, which suggests that this method could be complemented by other stochastic perturbation techniques in seasonal forecasting mode

Influence of phosphate and disinfection on the composition of biofilms produced from drinking water, as measured by fluorescence in situ hybridization

International audienceBiofilms were grown in annular reactors supplied with drinking water enriched with 235 µg C/L. Changes in the biofilms with ageing, disinfection, and phosphate treatment were monitored using fluorescence in situ hybridization. EUB338, BET42a, GAM42a, and ALF1b probes were used to target most bacteria and the alpha (α), beta (β), and gamma (γ) subclasses of Proteobacteria, respectively. The stability of biofilm composition was checked after the onset of colonization between T = 42 days and T = 113 days. From 56.0% to 75.9% of the cells detected through total direct counts with DAPI (4'-6-diamidino-2-phenylindole) were also detected with the EUB338 probe, which targets the 16S rRNA of most bacteria. Among these cells, 16.9%–24.7% were targeted with the BET42a probe, 1.8%–18.3% with the ALF1b probe, and <2.5% with the GAM42a probe. Phosphate treatment induced a significant enhancement to the proportion of γ-Proteobacteria (detected with the GAM42a probe), a group that contains many health-related bacteria. Disinfection with monochloramine for 1 month or chlorine for 3 days induced a reduction in the percentage of DAPI-stained cells that hybridized with the EUB338 probe (as expressed by percentages of EUB338 counts/DAPI) and with any of the ALF1b, BET42a, and GAM42a probes. The percentage of cells detected by any of the three probes (ALF1b+BET42a+GAM42a) tended to decrease, and reached in total less than 30% of the EUB338-hybridized cells. Disinfection with chlorine for 7 days induced a reverse shift; an increase in the percentage of EUB338 counts targeted by any of these three probes was noted, which reached up to 87%. However, it should be noted that the global bacterial densities (heterotrophic plate counts and total direct counts) tended to decrease over the duration of the experiment. Therefore, those bacteria that could be considered to resist 7 days of chlorination constituted a small part of the initial biofilm community, up to the point at which the other bacterial groups were destroyed by chlorination. The results suggest that there were variations in the kinetics of inactivation by disinfectant, depending on the bacterial populations involved.Key words: biofilm, phosphate, chlorine, monochloramine, FISH, drinking water

On the influence of ENSO on sudden stratospheric warmings

Using the extended ERA5 reanalysis and three state-of-the-art models, this study explores how El Niño-Southern Oscillation (ENSO) can influence the total frequency, seasonal cycle and preconditioning of sudden stratospheric warmings (SSWs). Reanalysis data shows that in the last seven decades, winters with SSWs were more common than winters without, regardless El Niño (EN) or La Niña (LN) occurrence or the ENSO/SSW definitions. In agreement with previous studies, our models tend to simulate a linear ENSO-SSW relationship, with more SSWs for EN, around mid-winter (January–February) as in reanalysis, and less for LN when compared to neutral conditions. Independently of ENSO, the main tropospheric precursor of SSWs appears to be an anomalous wave-like pattern over Eurasia, but it is dominated by wavenumber 1 (WN1) for EN and shows an enhanced wavenumber 2 (WN2) for LN. The differences in this Eurasian wave pattern, which is largely internally generated, emerge from the distinct configuration of the background, stationary wave pattern induced by ENSO in the North Pacific, favoring a stronger WN1 (WN2) component during EN (LN). Our results suggest that the ENSO-forced signal relies on modulating the seasonal-mean polar vortex strength, becoming weaker and more displaced (stronger and more stable) for EN (LN), while ENSO-unforced wave activity represents the ultimate trigger of SSWs. This supports the view that ENSO and SSWs are distinct sources of variability of the winter atmospheric circulation operating at different time-scales and may reconcile previous findings in this context.MEDSCOPE by JPI ClimateAEMET (España)ANR (FR)BSC (España)CMCC (IT)CNR (IT)IMR (BE)Météo-France (FR)Union EuropeaSpanish GRAVITOCASTthe Ramón y Cajal programmeDepto. de Física de la Tierra y AstrofísicaFac. de Ciencias FísicasTRUEpu

The role of climate forecasts in smallholder agriculture: Lessons from participatory research in two communities in Senegal

International audienc

Benefits of Increasing the Model Resolution for the Seasonal Forecast Quality in EC-Earth

Resolution in climate models is thought to be an important factor for advancing seasonal prediction capability. To test this hypothesis, seasonal ensemble reforecasts are conducted over 1993–2009 with the European community model EC-Earth in three configurations: standard resolution (~1° and ~60 km in the ocean and atmosphere models, respectively), intermediate resolution (~0.25° and ~60 km), and high resolution (~0.25° and ~39 km), the two latter configurations being used without any specific tuning. The model systematic biases of 2-m temperature, sea surface temperature (SST), and wind speed are generally reduced. Notably, the tropical Pacific cold tongue bias is significantly reduced, the Somali upwelling is better represented, and excessive precipitation over the Indian Ocean and over the Maritime Continent is decreased. In terms of skill, tropical SSTs and precipitation are better reforecasted in the Pacific and the Indian Oceans at higher resolutions. In particular, the Indian monsoon is better predicted. Improvements are more difficult to detect at middle and high latitudes. Still, a slight improvement is found in the prediction of the winter North Atlantic Oscillation (NAO) along with a more realistic representation of atmospheric blocking. The sea ice extent bias is unchanged, but the skill of the reforecasts increases in some cases, such as in summer for the pan-Arctic sea ice. All these results emphasize the idea that the resolution increase is an essential feature for forecast system development. At the same time, resolution alone cannot tackle all the forecast system deficiencies and will have to be implemented alongside new physical improvements to significantly push the boundaries of seasonal prediction.The research leading to these results has received funding from the EU Seventh Framework Programme FP7 (2007–2013) under Grant Agreements 308378 (SPECS), 603521 (PREFACE), and 607085 (EUCLEIA), the Horizon 2020 EU program under Grant Agreements 641727 (PRIMAVERA) and 641811 (IMPREX), and the ESA Climate Change Initiative (CCI) Living Planet Fellowship VERITAS-CCI. We acknowledge PRACE for awarding access to Marenostrum3 based in Spain at the Barcelona Supercomputing Center through the HiResClim project. We acknowledge the work of the developers of the s2dverification R-based package (http://cran.r-project. org/web/packages/s2dverification/index.html) and autosubmit workflow manager (https://pypi.python.org/ pypi/autosubmit/3.5.0). Paolo Davini acknowledges the funding from the European Union’s Horizon 2020 research and innovation programme COGNAC under the European Union Marie Sklodowska-Curie Grant Agreement 654942.Peer ReviewedPostprint (published version