Abstract. We investigate the representation of the Canary upwelling system (CUS) in six global coupled climate models operated at high and standard resolution as part of the High Resolution Model Intercomparison Project (HighResMIP). The models' performance in reproducing the observed CUS is assessed in terms of various upwelling indices based on sea surface temperature (SST), wind stress, and sea surface height, focusing on the effect of increasing model spatial resolution. Our analysis shows that possible improvement in upwelling representation due to the increased spatial resolution depends on the subdomain of the CUS considered. Strikingly, along the Iberian Peninsula region, which is the northernmost part of the CUS, the models show lower skill at higher resolution compared to their corresponding lower-resolution version in both components for all the indices analyzed in this study. In contrast, over the southernmost part of the CUS, from the north of Morocco to the Senegalese coast, the high-ocean- and high-atmosphere-resolution models simulate a more realistic upwelling than the standard-resolution models, which largely differ from the range of observational estimates. These results suggest that increasing resolution is not a sufficient condition to obtain a systematic improvement in the simulation of the upwelling phenomena as represented by the indices considered here, and other model improvements notably in terms of the physical parameterizations may also play a role.
Long-range empirical forecasts of North Atlantic anomalous conditions are issued, using sea ice concentration anomalies in the same region as predictors. Conditions in the North Atlantic are characterized by anomalies of sea surface temperature, of 850 hPa air temperature and of sea level pressure. Using the Singular Value Decomposition of the cross-covariance matrix between the sea ice field (the predictor) and each of the predictand variables, empirical models are built, and forecasts at lead times from 3 to 18 months are presented. The forecasts of the air temperature anomalies score the highest levels of the skill, while forecasts of the sea level pressure anomalies are the less sucessful ones.To investigate the sources of the forecast skill, we analyze their spatial patterns. In addition, we investigate the influence of major climatic signals on the forecast skill. In the case of the air temperature anomalies, the spatial pattern of the skill may be connected to El Niñ o Southern Oscillation (ENSO) influences. The ENSO signature is present in the predictor field, as shown in the composite analysis. The composite pattern indicates a higher (lower) sea ice concentration in the Labrador Sea and the opposite situation in the Greenland-Barents Seas during the warm (cold) phase of ENSO. The forecasts issued under the El Niñ o conditions show improved skill in the Labrador region, the Iberian Peninsula and south of Greenland for the lead times considered in this paper. For the Great Lakes region the skill increases when the predictor is under the influence of a cold phase. Some features in the spatial structure of the skill of the forecasts issued in the period of the Great Salinity Anomaly present similarities with those found for forecasts made during the cold phase of ENSO. The strength of the dependence on the Great Salinity Anomaly makes it very difficult to determine the influence of the North Atlantic Oscillation.
<p><span>The oceanic region located off the of the Iberian Peninsula at 43&#176;N to south of Senegal at about 10&#176;N, coasts is one of the most productive in the world in terms of marine ecosystems. This is due to the presence of the Canary Upwelling System (CUS). This upwelling region is separated into two distinct areas: the Iberian coast and the Northwest African coast. Improving our knowledge of the functioning and long term changes in the CUS is of crucial importance, since the much of the food resources and economy of neighboring countries greatly depends on its characteristics. Most of research efforts aimed at the understanding of the functioning of the CUS and its seasonal to long term variations, are based on observations and regional models operating at very high resolution. However, observational datasets based on satellite products, which are suitable to study upwelling systems, cover short periods of time, which does not allow for a robust estimate of long-term variations (i.e. climate change) of the upwellings and the associated mechanisms. The use of very high-resolution regional ocean models leads to a correct representation of the physical mechanisms associated to the upwellings, but the numerical experiments entail an important computational cost, which also limits the study of long-term changes. Standard coupled ocean-atmosphere models, such as those used in the international exercises like Coupled Model Experiment Phase (CMIP), provide an interesting alternative to study decadal to long-term changes in the upwellings. Recently, studies based on coupled models, focusing on the response of the upwellings to climate change, have received increasing attention. However, these studies show contradictory results on the question whether coastal upwelling will be more intense or weak in the next decades. One of the reasons for this uncertainty is the low resolution of climate models, making it difficult to properly resolve coastal zone processes. </span></p><p><span>The main goal of this study is to evaluate the ability of an ensemble of global coupled models in simulating the properties of the CUS (seasonal cycle, intensity and thermal signatures). The numerical experiments used here were performed within the H2020 PRIMAVERA European project, which is part of the HighResMIP initiative at European level. We will use pairs of models operating at diverse nominal resolutions under present-day climate conditions. Our objective will be to study the impact of model resolution in the representation of the CUS. </span></p><p>&#160;</p>
<p>Airplanes spend about 1% of cruise time in Moderate-Or-Greater (MOG) CAT (Sharman et al. 2006), which is defined as any turbulence occurring in the atmosphere away from a visible convective activity and which is particularly difficult to detect. MOG CAT events can injure passengers, cause structural damage to planes, and induce considerable economic loss. A major source of CAT is the Kelvin&#8211;Helmholtz instability (KHI), which is often induced by vertical wind shear associated with the jet stream and upper-level fronts. Recent studies have shown that under climate change, jet streams could be strengthened, and CAT frequency and intensity could significantly increase (Williams 2017). Assessing future CAT changes is a relatively new research topic and there are a lot of open questions. In particular, there is a need to understand the CAT trends in the present climate in atmospheric reanalysis and climate models and the mechanisms at play. The second step is to investigate the CAT sensitivity to global warming and the associated uncertainties.</p><p>In this study, we characterize present and future climate CAT trends in the Northern Hemisphere. For this purpose, we rely on a set of CAT indices computed with five different reanalysis datasets (among whom ERA5) and experiments performed by two CMIP6 climate models (CNRM-CM6-1 and IPSL-CM6A-LR).&#160;</p><p>In present climate, the analysis of the CAT indices over the last four decades shows that CAT is more frequent over the North Atlantic, the Pacific Northwest, the Himalayas and the Rocky Mountains. We find that the spatial distribution of CAT over the North Atlantic is strongly related to the variability of large-scale circulation patterns. In particular, the occurrence of CAT is clearly associated with the positive phase of the North Atlantic Oscillation (NAO+) and the Atlantic Ridge weather regimes. A significant positive trend of CAT frequency is found using reanalysis in different regions of the northern hemisphere. However, the signal-to-noise ratio estimated from the climate models is still very weak in the present climate except over Northeast Asia.</p><p>We find that positive trends of CAT frequency are enhanced in response to global warming for the ssp8.5 worst-case scenario over the midlatitudes at the level 200hPa. This is coherent with previous studies. However, results also suggest that CAT future changes highly depend on altitude level and the region considered. For example, over the North Atlantic, CAT frequency significantly increases at the 200hPa (about 11&#160;km) and 300hPa (about 9&#160;km) levels, while it decreases at the 250hPa (about 10 km) level. This highlights the importance of study future changes in the vertical structure of the atmosphere.</p><p>&#160;</p><p>Sharman R., Tebaldi C., Wiener G. et Wolff J., 2006, &#171;&#160;An Integrated Approach to Mid- and Upper-Level Turbulence Forecasting&#160;&#187;, <em>Weather and Forecasting</em>, vol.&#160;21, n<sup>o</sup>&#160;3, p. 268&#8209;287.</p><p>Williams Paul D., 2017, &#171;&#160;Increased light, moderate, and severe clear-air turbulence in response to climate change&#160;&#187;, <em>Advances in Atmospheric Sciences</em>, vol.&#160;34, n<sup>o</sup>&#160;5, p. 576&#8209;586.</p>
Sahelian rainfall presents large interannual variability which is partly controlled by the sea surface temperature anomalies (SSTa) over the eastern Mediterranean, equatorial Pacific and Atlantic oceans, making seasonal prediction of rainfall changes in Sahel potentially possible. However, it is not clear whether seasonal forecast models present skill to predict the Sahelian rainfall anomalies. Here, we consider the set of models from the North American Multi-model ensemble (NMME) and analyze their skill in predicting the Sahelian precipitation and address the sources of this skill.Results show that though the skill in predicting the Sahelian rainfall is generally low, it and can be mostly explained by a combination of how well models predict the SSTa in the Mediterranean and in the equatorial Pacific regions, and how well they simulate the teleconnections of these SSTa with Sahelian rainfall. Our results suggest that Sahelian rainfall skill is improved for those models in which the Pacific SST - Sahel rainfall teleconnection is correctly simulated. On the other hand, models present a good ability to reproduce the sign of the Mediterranean SSTa – Sahel teleconnection, albeit with underestimated amplitude due to an underestimation of the variance of the SSTa over this oceanic region. However, they fail to correctly predict the SSTa over this basin, which is the main reason for the poor Sahel rainfall skill in models. Therefore, results suggest models need to improve their ability to reproduce the variability of the SSTa over the Mediterranean as well as the teleconnections of Sahelian rainfall with Pacific and Mediterranean SSTa.
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