Coupled climate models used for long-term future climate projections and seasonal or decadal predictions share a systematic and persistent warm sea surface temperature (SST) bias in the tropical Atlantic. This study attempts to better understand the physical mechanisms responsible for the development of systematic biases in the tropical Atlantic using the so-called Transpose-CMIP protocol in a multi-model context. Six global climate models have been used to perform seasonal forecasts starting both in May and February over the period 2000-2009. In all models, the growth of SST biases is rapid. Significant biases are seen in the first month of forecast and, by 6 months, the root-mean-square SST bias is 80% of the climatological bias. These control experiments show that the equatorial warm SST bias is not driven by surface heat flux biases in all models, whereas in the southeastern Atlantic the solar heat flux could explain the setup of an initial warm bias in the first few days. A set of sensitivity experiments with prescribed wind stress confirm the leading role of wind stress biases in driving the equatorial SST bias, even if the amplitude of the SST bias is model dependent. A reduced SST bias leads to a reduced precipitation bias locally, but there is no robust remote effect on West African Monsoon rainfall. Over the southeastern part of the basin, local wind biases tend to have an impact on the local SST bias (except in the high resolution model). However, there is also a non-local effect of equatorial wind correction in two models. This can be explained by sub-surface advection of water from the equator, which is colder when the bias in equatorial wind stress is corrected. In terms of variability, it is also shown that improving the mean state in the equatorial Atlantic leads to a beneficial intensification of the Bjerknes feedback loop. In conclusion, we show a robust effect of wind stress biases on tropical mean climate and variability in multiple climate models.
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. © 2017 Springer-Verlag Berlin Heidelber
The substantial warm sea surface temperature bias in the eastern Tropical Atlantic reported in most CMIP5 climate simulations with various models, in particular along the coast of Namibia and Angola, remains an issue in more recent and CMIP6-ready versions of climate models such as EC-Earth. A complete and original set of experiments with EC-Earth3.1 is performed to investigate the causes and mechanisms responsible for the emergence and persistence of this bias. The fully-developed bias is studied in a historical experiment that has reached quasi-equilibrium, while retrospective prediction experiments are used to highlight the development/growth from an observed initial state. Prediction experiments are performed at both low and high resolution to assess the possible dependence of the bias on horizontal resolution. Standalone experiments with the ocean and the atmosphere components of EC-Earth are also analyzed to separate the respective contributions of the ocean and atmosphere to the development of the bias. EC-Earth3.1 exhibits a bias similar to that reported in most climate models that took part in CMIP5. The magnitude of this bias, however, is weaker than most CMIP5 models by few degrees. Increased horizontal resolution only leads to a minor reduction of the bias in EC-Earth. The warm SST bias is found to be the result of an excessive solar absorption in the ocean mixed layer, which can be linked to the excessive solar insolation due to unrealistically low cloud cover, and the absence of spatial and temporal variability of the biological productivity in the ocean component. The warm SST bias is further linked to deficient turbulent vertical mixing of cold water to the mixed layer. Our study points at a need for better representation of clouds in the vicinity of eastern boundaries in atmosphere models, and better representation of solar penetration and turbulent mixing in the ocean models in order to eliminate the Tropical Atlantic biases.
There is now a wide range of forecasts and observations of seasonal climatic conditions that can be used across a range of application sectors, including hydrological risk forecasting, planning and management. As we rely more on seasonal climate forecasts, it becomes essential to also assess its quality to ensure its intended use. In this study, we provide the most comprehensive assessment of seasonal temperature and precipitation ensemble forecasts of the EUROSIP multi-model forecasting system over Europe. The forecasts from the four individual climate models within the EUROSIP are assessed using both deterministic and probabilistic approaches. One equally and two unequally Weighted Multi-Models (WMMs) are also constructed from the individual models, for both climate variables, and their respective forecasts are also assessed. Consistent with existing literature, we find limited seasonal climate prediction skill over Europe. A simple equally WMM system performs better than both unequally WMM combination systems. However, the equally WMM system does not always outperform the single best model within the EUROSIP multi-model. Based on the results, it is recommended to assess seasonal temperature and precipitation forecast of individual climate models as well as their multi-model mean for a comprehensive overview of the forecast skill.
Seasonal crop yield forecasting represents an important source of information to maintain market stability, minimise socio-economic impacts of crop losses and guarantee humanitarian food assistance, while it fosters the use of climate information favouring adaptation strategies. As climate variability and extremes have significant influence on agricultural production, the early prediction of severe weather events and unfavourable conditions can contribute to the mitigation of adverse effects. Seasonal climate forecasts provide additional value for agricultural applications in several regions of the world. However, they currently play a very limited role in supporting agricultural decisions in Europe, mainly due to the poor skill of relevant surface variables. Here we show how a combined stress index (CSI), considering both drought and heat stress in summer, can predict maize yield in Europe and how land-surface initialised seasonal climate forecasts can be used to predict it. The CSI explains on average nearly 53% of the inter-annual maize yield variability under observed climate conditions and shows how concurrent heat stress and drought events have influenced recent yield anomalies. Seasonal climate forecast initialised with realistic land-surface achieves better (and marginally useful) skill in predicting the CSI than with climatological land-surface initialisation in south-eastern Europe, part of central Europe, France and Italy.
International audienceIn this study, the impact of the ocean-atmosphere coupling on the atmospheric mean state over the Indian Ocean and the Indian Summer Monsoon (ISM) is examined in the framework of the SINTEX-F2 coupled model through forced and coupled control simulations and several sensitivity coupled experiments. During boreal winter and spring, most of the Indian Ocean biases are common in forced and coupled simulations, suggesting that the errors originate from the atmospheric model, especially a dry islands bias in the Maritime Continent. During boreal summer, the air-sea coupling decreases the ISM rainfall over South India and the monsoon strength to realistic amplitude, but at the expense of important degradations of the rainfall and Sea Surface Temperature (SST) mean states in the Indian Ocean. Strong SST biases of opposite sign are observed over the western (WIO) and eastern (EIO) tropical Indian Ocean. Rainfall amounts over the ocean (land) are systematically higher (lower) in the northern hemisphere and the south equatorial Indian Ocean rainfall band is missing in the control coupled simulation. During boreal fall, positive dipole-like errors emerge in the mean state of the coupled model, with warm and wet (cold and dry) biases in the WIO (EIO), suggesting again a significant impact of the SST errors. The exact contributions and the distinct roles of these SST errors in the seasonal mean atmospheric state of the coupled model have been further assessed with two sensitivity coupled experiments, in which the SST biases are replaced by observed climatology either in the WIO (warm bias) or EIO (cold bias). The correction of the WIO warm bias leads to a global decrease of rainfall in the monsoon region, which confirms that the WIO is an important source of moisture for the ISM. On the other hand, the correction of the EIO cold bias leads to a global improvement of precipitation and circulation mean state during summer and fall. Nevertheless, all these improvements due to SST corrections seem drastically limited by the atmosphere intrinsic biases, including prominently the unimodal oceanic position of the ITCZ (Inter Tropical Convergence Zone) during summer and the enhanced westward wind stress along the equator during fall
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