Biases in climate model simulations introduce biases in subsequent impact simulations. Therefore, bias correction methods are operationally used to post-process regional climate projections. However, many problems have been identified, and some researchers question the very basis of the approach. Here we demonstrate that a typical cross-validation is unable to identify improper use of bias correction. Several examples show the limited ability of bias correction to correct and to downscale variability, and demonstrate that bias correction can cause implausible climate change signals. Bias correction cannot overcome major model errors, and naive application might result in ill-informed adaptation decisions. We conclude with a list of recommendations and suggestions for future research to reduce, post-process, and cope with climate model biases

No abstract

The eastern boundaries of the tropical and subtropical oceans are regions of high biological productivity that support some of the world's largest fisheries. They also feature extensive stratocumulus cloud decks that play a pivotal role in the response of the climate system to greenhouse gas forcing. Global climate models experience great difficulties simulating eastern boundary regions, with one of the most notable shortcomings being warm sea-surface temperature biases that often exceed 5 K. These model biases are due to several reasons. (1) Weaker than observed alongshore winds lead to an underrepresentation of upwelling and alongshore currents and the cooling associated with them. (2) Stratocumulus decks and their effects on shortwave radiation are underpredicted in the models. (3) The offshore transport of cool waters by mesoscale eddies is not adequately represented by global models due to insufficient resolution. (4) The sharp vertical temperature gradient separating the warm upper ocean layer from the deep ocean is too diffuse in the models. More work will be required to assess the relative importance of these error sources and to find ways of mitigating them. Coordinated multi-model experiments are vital to achieve this goal, as are enhanced ocean and atmosphere observations of the eastern boundary regions. To what extent eastern ocean biases compromise the models' ability to produce accurate seasonal predictions, and climate change projections should be another focus of research efforts.

No abstract

Sea surface temperature (SST) variability in the tropical Atlantic Ocean strongly impacts the climate on the surrounding continents. On interannual time scales, highest SST variability occurs in the eastern equatorial region and off the coast of southwestern Africa. The pattern of SST variability resembles the Pacific El Niño, but features notable differences, and has been discussed in the context of various climate modes, that is, reoccurring patterns resulting from particular interactions in the climate system. Here, we attempt to reconcile those different definitions, concluding that almost all of them are essentially describing the same mode that we refer to as the “Atlantic Niño.” We give an overview of the mechanisms that have been proposed to underlie this mode, and we discuss its interaction with other climate modes within and outside the tropical Atlantic. The impact of Atlantic Niño‐related SST variability on rainfall, in particular over the Gulf of Guinea and north eastern South America is also described. An important aspect we highlight is that the Atlantic Niño and its teleconnections are not stationary, but subject to multidecadal modulations. Simulating the Atlantic Niño proves a challenge for state‐of‐the‐art climate models, and this may be partly due to the large mean state biases in the region. Potential reasons for these model biases and implications for seasonal prediction are discussed. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models