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Plant phenology plays a fundamental role in land–atmosphere interactions, and its variability and variations are an indicator of climate and environmental changes. For this reason, current land surface models include phenology parameterizations and related biophysical and biogeochemical processes. In this work, the climatology of the beginning and end of the growing season, simulated by the land component of seven state-of-the-art European Earth system models participating in the CMIP6, is evaluated globally against satellite observations. The assessment is performed using the vegetation metric leaf area index and a recently developed approach, named four growing season types. On average, the land surface models show a 0.6-month delay in the growing season start, while they are about 0.5 months earlier in the growing season end. The difference with observation tends to be higher in the Southern Hemisphere compared to the Northern Hemisphere. High agreement between land surface models and observations is exhibited in areas dominated by broadleaf deciduous trees, while high variability is noted in regions dominated by broadleaf deciduous shrubs. Generally, the timing of the growing season end is accurately simulated in about 25 % of global land grid points versus 16 % in the timing of growing season start. The refinement of phenology parameterization can lead to better representation of vegetation-related energy, water, and carbon cycles in land surface models, but plant phenology is also affected by plant physiology and soil hydrology processes. Consequently, phenology representation and, in general, vegetation modelling is a complex task, which still needs further improvement, evaluation, and multi-model comparison.

Seasonal predictions leverage on predictable or persistent components of the Earth system that can modify the state of the atmosphere. The land surface provides predictability through various mechanisms, including snow cover, with particular reference to Autumn snow cover over the Eurasian continent. The snow cover alters the energy exchange between surface and atmosphere and induces a diabatic cooling that in turn can affect the atmosphere locally and remotely. Lagged relationships between snow cover in Eurasia and atmospheric modes of variability in the Northern Hemisphere have been documented but are deemed to be non-stationary and climate models typically do not reproduce observed relationships with consensus. The role of the snow in recent dynamical seasonal forecasts is therefore unclear. Here we assess the role of Autumn Eurasian snow cover in a set of five operational seasonal forecasts with large ensemble size and high resolution and with the help of targeted idealised simulations. Forecast systems reproduce realistically regional changes of the surface energy balance. Retrospective forecasts and idealised sensitivity experiments identify a coherent change of the circulation in the Northern Hemisphere. The main features of the atmospheric response are a wave-train downstream over the Pacific and North America and a signal in the Arctic. The latter does not emerge in reanalysis data but is compatible with a lagged but weak and fast feedback from the snow to the Arctic Oscillation.

El Niño Southern Oscillation (ENSO) represents the major driver of interannual climate variability at global scale. Observational and model-based studies have fostered a long-standing debate on the shape and intensity of the ENSO influence over the Euro-Mediterranean sector. Indeed, the detection of this signal is strongly affected by the large internal variability that characterizes the atmospheric circulation in the North Atlantic–European (NAE) region. This study explores if and how the low-frequency variability of North Pacific sea surface temperature (SST) may impact the El Niño-NAE teleconnection in late winter, which consists of a dipolar pattern between middle and high latitudes. A set of idealized atmosphere-only experiments, prescribing different phases of the anomalous SST linked to the Pacific Decadal Oscillation (PDO) superimposed onto an El Niño-like forcing in the tropical Pacific, has been performed in a multi-model framework, in order to assess the potential modulation of the positive ENSO signal. The modelling results suggest, in agreement with observational estimates, that the PDO negative phase (PDO−) may enhance the amplitude of the El Niño-NAE teleconnection, while the dynamics involved appear to be unaltered. On the other hand, the modulating role of the PDO positive phase (PDO+) is not reliable across models. This finding is consistent with the atmospheric response to the PDO itself, which is robust and statistically significant only for PDO−. Its modulation seems to rely on the enhanced meridional SST gradient and the related turbulent heat-flux released along the Kuroshio–Oyashio extension. PDO− weakens the North Pacific jet, whereby favoring more poleward propagation of wave activity, strengthening the El Niño-forced Rossby wave-train. These results imply that there might be conditional predictability for the interannual Euro-Mediterranean climate variability depending on the background state. This work has been performed in the framework of the MEDSCOPE (MEDiterranean Services Chain based on climate PrEdictions) ERA4CS project (grant agreement no. 690462) funded by the European Union. J.G.-S. was supported by the “Ramón y Cajal” programme (RYC-2016-21181). F.M.P. was partially supported by the Spanish DANAE (CGL2015-68342-R) and GRAVITOCAST (ERC2018-092835) projects. We greatly thank the two anonymous reviewers for the insightful and constructive comments and suggestions.

Euro‐Mediterranean Centre on Climate Change coupled climate model (CMCC‐CM2) represents the new family of the global coupled climate models developed and used at CMCC. It is based on the atmospheric, land and sea ice components from the Community Earth System Model coupled with the global ocean model Nucleus for European Modeling of the Ocean. This study documents the model components, the coupling strategy, particularly for the oceanic, atmospheric, and sea ice components, and the overall model ability in reproducing the observed mean climate and main patterns of interannual variability. As a first step toward a more comprehensive, process‐oriented, validation of the model, this work analyzes a 200‐year simulation performed under constant forcing corresponding to present‐day climate conditions. In terms of mean climate, the model is able to realistically reproduce the main patterns of temperature, precipitation, and winds. Specifically, we report improvements in the representation of the sea surface temperature with respect to the previous version of the model. In terms of mean atmospheric circulation features, we notice a realistic simulation of upper tropospheric winds and midtroposphere geopotential eddies. The oceanic heat transport and the Atlantic meridional overturning circulation satisfactorily compare with present‐day observations and estimates from global ocean reanalyses. The sea ice patterns and associated seasonal variations are realistically reproduced in both hemispheres, with a better skill in winter. Main weaknesses of the simulated climate are related with the precipitation patterns, specifically in the tropical regions with large dry biases over the Amazon basin. Similarly, the seasonal precipitation associated with the monsoons, mostly over Asia, is weaker than observed. The main patterns of interannual variability in terms of dominant empirical orthogonal functions are faithfully reproduced, mostly in the Northern Hemisphere winter. In the tropics the main teleconnection patterns associated with El Niño–Southern Oscillation and with the Indian Ocean Dipole are also in good agreement with observations.

Ensembles of retrospective 2-month dynamical forecasts initiated on 1 May are used to predict the onset of the Indian summer monsoon (ISM) for the period 1989–2005. The subseasonal predictions (SSPs) are based on a coupled general circulation model and recently they have been upgraded by the realistic initialization of the atmosphere with initial conditions taken from reanalysis. Two objective large-scale methods based on dynamical-circulation and hydrological indices are applied to detect the ISM onset. The SSPs show some skill in forecasting earlier-than-normal ISM onsets, while they have difficulty in predicting late onsets. It is shown that significant contribution to the skill in forecasting early ISM onsets comes from the newly developed initialization of the atmosphere from reanalysis. On one hand, atmospheric initialization produces a better representation of the atmospheric mean state in the initial conditions, leading to a systematically improved monsoon onset sequence. On the other hand, the initialization of the atmosphere allows some skill in forecasting the northward-propagating intraseasonal wind and precipitation anomalies over the tropical Indian Ocean. The northward-propagating intraseasonal modes trigger the monsoon in some early-onset years. The realistic phase initialization of these modes improves the forecasts of the associated earlier-than-normal monsoon onsets. The prediction of late onsets is not noticeably improved by the initialization of the atmosphere. It is suggested that late onsets of the monsoon are too far away from the start date of the forecasts to conserve enough memory of the intraseasonal oscillation (ISO) anomalies and of the improved representation of the mean state in the initial conditions.

African tropical forests of the equatorial belt might receive significant input of extra nitrogen derived from biomass burning occurring in the north savanna belt and transported equatorward by northeastern winds. In order to test this hypothesis an experiment was set up in a tropical rain forest in the Ankasa Game Reserve and Nini-Suhien National Park (Ghana) aimed at quantifying magnitude and seasonal variability of concentrations of N compounds, present as gas and aerosol (dry nitrogen) or in the rainfall (bulk nitrogen), over the studied forest; and relating their seasonal variability to trends of local and regional winds and rainfall and to variations of fire events in the region. Three DELTA systems, implemented for monthly measurements of NO2, were mounted over a tower at 45 m height, 20 m above forest canopy to sample gas (NH3, NO2, HNO3, HCl, SO2) and aerosol (NH4+, NO3−, and several ions), together with three tanks for bulk rainfall collection (to analyze NH4+, NO3− and ion concentration). The tower was provided with a sonic anemometer to estimate local wind data. The experiment started in October 2011 and data up to October 2012 are presented. To interpret the observed seasonal trends of measured compounds, local and regional meteo data and regional satellite fire data were analyzed. The concentration of N compounds significantly increased from December to April, during the drier period, peaking from December to February when NE winds (the Harmattan) were moving dry air masses over the west-central African region, and the Intertropical Convergence Zone (ITCZ) was at its minimum latitude over the Equator. This period also coincided with fire peaks in the whole region. On the contrary, N concentration in gas, aerosol and rain decreased from May to October when prevalent winds arrived from the sea (southeast), during the monsoon period. Both ionic compositions of rain and analysis of local wind direction showed a significant and continuous presence of see breeze at site. The ionic composition of rainwater resulted much closer to seawater and poorer in N compounds from May to October.

The impact of land surface and atmosphere initialization on the forecast skill of a seasonal prediction system is investigated, and an effort to disentangle the role played by the individual components to the global predictability is done, via a hierarchy of seasonal forecast experiments performed under different initialization strategies. A realistic atmospheric initial state allows an improved equilibrium between the ocean and overlying atmosphere, increasing the model predictive skill in the ocean. In fact, in regions characterized by strong air–sea coupling, the atmosphere initial condition affects forecast skill for several months. In particular, the ENSO region, eastern tropical Atlantic, and North Pacific benefit significantly from the atmosphere initialization. On the mainland, the effect of atmospheric initial conditions is detected in the early phase of the forecast, while the quality of land surface initialization affects forecast skill in the following seasons. Winter forecasts in the high-latitude plains benefit from the snow initialization, while the impact of soil moisture initial state is particularly effective in the Mediterranean region and central Asia. However, the initialization strategy based on the full value technique may not be the best choice for land surface, since soil moisture is a strongly model-dependent variable: in fact, initialization through land surface reanalysis does not systematically guarantee a skill improvement. Nonetheless, using a different initialization strategy for land, as opposed to atmosphere and ocean, may generate inconsistencies. Overall, the introduction of a realistic initialization for land and atmosphere substantially increases skill and accuracy. However, further developments in the procedure for land surface initialization are required for more accurate seasonal forecasts.

The surface ocean explains a considerable part of the inter-annual Tropical Atlantic variability. The present work makes use of observational datasets to investigate the effect of freshwater flow on sea surface salinity (SSS) and temperature (SST) in the Gulf of Guinea. In particular, the Congo River discharges a huge amount of freshwater into the ocean, affecting SSS in the Eastern Equatorial Atlantic (EEA) and stratifying the surface layers. The hypothesis is that an excess of river runoff emphasize stratification, influencing the ocean temperature. In fact, our findings show that SSTs in the Gulf of Guinea are warmer in summers following an anomalously high Congo spring discharge. Vice versa, when the river discharges low freshwater, a cold anomaly appears in the Gulf. The response of SST is not linear: temperature anomalies are considerable and long-lasting in the event of large freshwater flow, while in dry years they are less remarkable, although still significant. An excess of freshwater seems able to form a barrier layer, which inhibits vertical mixing and the entrainment of the cold thermocline water into the surface. Other processes may contribute to SST variability, among which the net input of atmospheric freshwater falling over EEA. Likewise the case of continental runoff from Congo River, warm anomalies occur after anomalously rainy seasons and low temperatures follow dry seasons, confirming the effect of freshwater on SST. However, the two sources of freshwater anomaly are not in phase, so that it is possible to split between atypical SST following continental freshwater anomalies and rainfall anomalies. Also, variations in air-sea fluxes can produce heating and cooling of the Gulf of Guinea. Nevertheless, atypical SSTs cannot be ascribed to fluxes, since the temperature variation induced by them is not sufficient to explain the SST anomalies appearing in the Gulf after anomalous peak discharges. The interaction processes between river runoff, sea surface salinity and temperature play an effective role in the interannual variability in the EEA region. Our results add a new source of variability in the area, which was often neglected by previous studies.

The discharge of freshwater into oceans represents a fundamental process in the global climate system, and this flux is taken into account in simulations with general circulation models (GCMs). Moreover, the availability of realistic river routing schemes is a powerful instrument to assess the validity of land surface components, which have been recognized to be crucial for the global climate simulation. In this study, surface and subsurface runoff generated by the 13 land surface schemes (LSSs) participating in the Second Global Soil Wetness Project (GSWP-2) are used as input fields for the Hydrology Discharge (HD) routing model to simulate discharge for 30 of the world’s largest rivers. The simplest land surface models do not provide a good representation of runoff, and routed river flows using these inputs are affected by many biases. On the other hand, HD shows the best simulations when forced by two of the more sophisticated schemes. The multimodel ensemble GSWP-2 generates the best phasing of the annual cycle as well as a good representation of absolute values, although the ensemble mean tends to smooth the peaks. Finally, the intermodel comparison shows the limits and deficiencies of a velocity-constant routing model such as HD, particularly in the phase of mean annual discharge. The second part of the study assesses the sensitivity of river discharge to the variation of external meteorological forcing. The Center for Ocean–Land–Atmosphere Studies version of the SSiB model is constrained with different meteorological fields and the resulting runoff is used as input for HD. River flow is most sensitive to precipitation variability, but changes in radiative forcing affect discharge as well, presumably because of the interaction with evaporation. Also, this analysis provides an estimate of the sensitivity of river discharge to precipitation variations. A few areas (e.g., central and eastern Asia, the Mediterranean, and much of the United States) show a magnified response of river discharge to a given percentage change in precipitation. Hence, an amplified effect of droughts as indicated by the consensus of climate change predictions may occur in places such as the Mediterranean. Conversely, increasing summer precipitation foreseen in places like southern and eastern Asia may amplify floods in these poor and heavily populated regions. Globally, a 1% fluctuation in precipitation forcing results in an average 2.3% change in discharge. These results can be used for the definition and assessment of new strategies for land use and water management in the near future.

Vegetation phenology and its variability have substantial influence on land-atmosphere interaction, and changes in growing season length are additional indicators of climate change impacts on ecosystems. For these reasons, global land surface models are routinely evaluated in order to assess their ability to reproduce the observed phenological variability. In this work, we present a new approach that integrates a wider spectrum of growing season modes, in order to better describe the observed variability in vegetation growing season onset and offset, as well as assess the ability of state-of-the-art land surface models to capture this variability at the global scale. The method is applied to the Community Land Model version 4.5 (CLM4.5) simulations and LAI3g satellite observation. The comparison between data and model outputs shows that CLM4.5 is capable of reproducing the growing season features in the Northern Hemisphere midlatitude and high latitude, but also displays its limitations in areas where water availability acts as the main driver of vegetation phenological activity. Besides, the new approach allows evaluating land surface models in capturing multigrowing-season phenology. In this regard, CLM4.5 proves its ability in reproducing the two-growing-season cycles in the Horn of Africa. In general, the new methodology expands the area of analysis from northern midlatitude and high latitude to the global continental areas and allows to assess the vegetation response to the ongoing climate change in a larger variety of ecosystems, ranging from semiarid regions to rain forests, passing through temperate deciduous and boreal evergreen forests.