Oceanic origin of southeast tropical Atlantic biases

Most coupled general circulation models suffer from a prominent warm sea surface temperature bias in the southeast tropical Atlantic Ocean off the coast of Africa. The origin of the bias is not understood and remains highly controversial. Previous studies suggest that the origin of the bias stems from systematic errors of atmospheric models in simulating surface heat flux and coastal wind, or poorly simulated coastal upwelling. In this study, we show, using different reanalysis and observational data sets combined with a set of eddy-resolving regional ocean model simulations, that systematic errors in ocean models also make a significant contribution to the bias problem. In particular (1) the strong warm bias at the Angola-Benguela front that is maintained by the local wind and the convergence of Angola and Benguela Currents is caused by an overshooting of the Angola Current in ocean models and (2) the alongshore warm bias to the south of the front is caused by ocean model deficiencies in simulating the sharp thermocline along the Angola coast, which is linked to biases in the equatorial thermocline, and the complex circulation system within the Benguela upwelling zone.     

Development of warm SST errors in the southern tropical Atlantic in CMIP5 decadal hindcasts

SST errors in the tropical Atlantic are large and systematic in current coupled general-circulation models. We analyse the growth of these errors in the region of the south-eastern tropical Atlantic in initialised decadal hindcasts integrations for three of the models participating in the Coupled Model Inter-comparison Project 5. A variety of causes for the initial bias development are identified, but a crucial involvement is found, in all cases considered, of ocean-atmosphere coupling for their maintenance. These involve an oceanic “bridge” between the Equator and the Benguela-Angola coastal seas which communicates sub-surface ocean anomalies and constitutes a coupling between SSTs in the south-eastern tropical Atlantic and the winds over the Equator. The resulting coupling between SSTs, winds and precipitation represents a positive feedback for warm SST errors in the south-eastern tropical Atlantic.     

Equatorial Atlantic variability and its relation to mean state biases in CMIP5

Coupled general circulation model (GCM) simulations participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are analyzed with respect to their performance in the equatorial Atlantic. In terms of the mean state, 29 out of 33 models examined continue to suffer from serious biases including an annual mean zonal equatorial SST gradient whose sign is opposite to observations. Westerly surface wind biases in boreal spring play an important role in the reversed SST gradient by deepening the thermocline in the eastern equatorial Atlantic and thus reducing upwelling efficiency and SST cooling in the following months. Both magnitude and seasonal evolution of the biases are very similar to what was found previously for CMIP3 models, indicating that improvements have only been modest. The weaker than observed equatorial easterlies are also simulated by atmospheric GCMs forced with observed SST. They are related to both continental convection and the latitudinal position of the intertropical convergence zone (ITCZ). Particularly the latter has a strong influence on equatorial zonal winds in both the seasonal cycle and interannual variability. The dependence of equatorial easterlies on ITCZ latitude shows a marked asymmetry. From the equator to 15°N, the equatorial easterlies intensify approximately linearly with ITCZ latitude. When the ITCZ is south of the equator, on the other hand, the equatorial easterlies are uniformly weak. Despite serious mean state biases, several models are able to capture some aspects of the equatorial mode of interannual SST variability, including amplitude, pattern, phase locking to boreal summer, and duration of events. The latitudinal position of the boreal spring ITCZ, through its influence on equatorial surface winds, appears to play an important role in initiating warm events.     

CMIP5模式对太阳活动准11年周期激发类拉尼娜型海温的评估

利用24个第五次耦合模式比较计划(Coupled Model Intercomparison Project Phase 5,CMIP5)历史试验资料,本文评估了在太阳年代际尺度准11 a周期变化下能否激发出热带太平洋显著的类拉尼娜型海表温度异常的模拟能力。再分析资料分析结果表明,“自下而上”机制解释了在太阳强迫偏强的年份,热带太平洋更容易呈现出显著的类拉尼娜型海温异常。CMIP5模式的评估结果显示,有2/5的模式可以基本再现再分析资料中太阳强迫影响下的热带东太平洋海温负异常,这些模式分为类拉尼娜组;而另有3/5的模式模拟出了相反的信号,分为类厄尔尼诺组。为了进一步探讨CMIP5模式模拟能力不同的原因,本文分析了“自下而上”机制在模式中的表现。“自下而上”机制可分为蒸发过程和海洋恒温(thermostat)过程。结果表明,模式能否模拟出类拉尼娜型海温响应取决于thermostat过程的强弱,其中类拉尼娜组的thermostat过程更强;而蒸发过程没有起到关键作用。     

Understanding the sources of Caribbean precipitation biases in CMIP3 and CMIP5 simulations

We assess the ability of Global Climate Models participating in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) to simulate observed annual precipitation cycles over the Caribbean. Compared to weather station records and gridded observations, we find that both CMIP3 and CMIP5 models can be grouped into three categories: (1) models that correctly simulate a bimodal distribution with two rainfall maxima in May–June and September–October, punctuated by a mid-summer drought (MSD) in July–August; (2) models that reproduce the MSD and the second precipitation maxima only; and (3) models that simulate only one precipitation maxima, beginning in early summer. These categories appear related to model simulation of the North Atlantic Subtropical High (NASH) and sea surface temperature (SST) in the Caribbean Sea and Gulf of Mexico. Specifically, models in category 2 tend to anticipate the westward expansion of the NASH into the Caribbean in early summer. Early onset of NASH results in strong moisture divergence and MSD-like conditions at the time of the May–June observed precipitation maxima. Models in category 3 tend to have cooler SST across the region, particularly over the central Caribbean and the Gulf of Mexico, as well as a weaker Caribbean low-level jet accompanying a weaker NASH. In these models, observed June-like patterns of moisture convergence in the central Caribbean and the Central America and divergence in the east Caribbean and the Gulf of Mexico persist through September. This analysis suggests systematic biases in model structure may be responsible for biases in observed precipitation variability over the Caribbean and more confidence may be placed in the precipitation simulated by the GCMs that are able to correctly simulate seasonal cycles of SST and NASH.     

Weakened SST variability in the tropical Atlantic Ocean since 2000

A prominent weakening in equatorial Atlantic sea surface temperature (SST) variability, occurring around the year 2000, is investigated by means of observations, reanalysis products and the linear recharge oscillator (ReOsc) model. Compared to the time period 1982–1999, during 2000–2017 the May–June–July SST variability in the eastern equatorial Atlantic has decreased by more than 30%. Coupled air–sea feedbacks, namely the positive Bjerknes feedback and the negative net heat flux damping are important drivers for the equatorial Atlantic interannual SST variability. We find that the Bjerknes feedback weakened after 2000 while the net heat flux damping increased. The weakening of the Bjerknes feedback does not appear to be fully explainable by changes in the mean state of the tropical Atlantic. The increased net heat flux damping is related to an enhanced response of the latent heat flux to the SST anomalies (SSTa). Strengthened trade winds as well as warmer SSTs are suggested to increase the air–sea specific humidity difference and hence, enhancing the latent heat flux response to SSTa. A combined effect of those two processes is proposed to be responsible for the weakened SST variability in the eastern equatorial Atlantic. The ReOsc model supports the link between reduced SST variability, weaker Bjerknes feedback and stronger net heat flux damping.     

Changes in temperature and precipitation extremes in the CMIP5 ensemble

Twenty-year temperature and precipitation extremes and their projected future changes are evaluated in an ensemble of climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), updating a similar study based on the CMIP3 ensemble. The projected changes are documented for three radiative forcing scenarios. The performance of the CMIP5 models in simulating 20-year temperature and precipitation extremes is comparable to that of the CMIP3 ensemble. The models simulate late 20th century warm extremes reasonably well, compared to estimates from reanalyses. The model discrepancies in simulating cold extremes are generally larger than those for warm extremes. Simulated late 20th century precipitation extremes are plausible in the extratropics but uncertainty in extreme precipitation in the tropics and subtropics remains very large, both in the models and the observationally-constrained datasets. Consistent with CMIP3 results, CMIP5 cold extremes generally warm faster than warm extremes, mainly in regions where snow and sea-ice retreat with global warming. There are tropical and subtropical regions where warming rates of warm extremes exceed those of cold extremes. Relative changes in the intensity of precipitation extremes generally exceed relative changes in annual mean precipitation. The corresponding waiting times for late 20th century extreme precipitation events are reduced almost everywhere, except for a few subtropical regions. The CMIP5 planetary sensitivity in extreme precipitation is about 6 %/°C, with generally lower values over extratropical land.     

SST and circulation trend biases cause an underestimation of European precipitation trends

Clear precipitation trends have been observed in Europe over the past century. In winter, precipitation has increased in north-western Europe. In summer, there has been an increase along many coasts in the same area. Over the second half of the past century precipitation also decreased in southern Europe in winter. An investigation of precipitation trends in two multi-model ensembles including both global and regional climate models shows that these models fail to reproduce the observed trends. In many regions the model spread does not cover the trend in the observations. In contrast, regional climate model (RCM) experiments with observed boundary conditions reproduce the observed precipitation trends much better. The observed trends are largely compatible with the range of uncertainties spanned by the ensemble, indicating that the boundary conditions of RCMs are responsible for large parts of the trend biases. We find that the main factor in setting the trend in winter is atmospheric circulation, for summer sea surface temperature (SST) is important in setting precipitation trends along the North Sea and Atlantic coasts. The causes of the large trends in atmospheric circulation and summer SST are not known. For SST there may be a connection with the well-known ocean circulation biases in low-resolution ocean models. A quantitative understanding of the causes of these trends is needed so that climate model based projections of future climate can be corrected for these precipitation trend biases.     

The Atlantic ITCZ bias in CMIP5 models

Climate Dynamics - Precipitation over the tropical Atlantic in 24 atmospheric models is analyzed using an object-based approach, which clusters rainy areas in the models as precipitation objects...     

Implications of CMIP3 model biases and uncertainties for climate projections in the western tropical Pacific

Regional climate projections in the Pacific region are potentially sensitive to a range of existing model biases. This study examines the implications of coupled model biases on regional climate projections in the tropical western Pacific. Model biases appear in the simulation of the El Niño Southern Oscillation, the location and movement of the South Pacific Convergence Zone, rainfall patterns, and the mean state of the ocean–atmosphere system including the cold tongue bias and erroneous location of the edge of the Western Pacific warm pool. These biases are examined in the CMIP3 20th century climate models and used to provide some context to the uncertainty in interpretations of regional-scale climate projections for the 21st century. To demonstrate, we provide examples for two island nations that are located in different climate zones and so are affected by different biases: Nauru and Palau. We discuss some of the common approaches to analyze climate projections and whether they are effective in reducing the effect of model biases. These approaches include model selection, calculating multi model means, downscaling and bias correcting.     

Climate change hotspots in the CMIP5 global climate model ensemble

We use a statistical metric of multi-dimensional climate change to quantify the emergence of global climate change hotspots in the CMIP5 climate model ensemble. Our hotspot metric extends previous work through the inclusion of extreme seasonal temperature and precipitation, which exert critical influence on climate change impacts. The results identify areas of the Amazon, the Sahel and tropical West Africa, Indonesia, and the Tibetan Plateau as persistent regional climate change hotspots throughout the 21st century of the RCP8.5 and RCP4.5 forcing pathways. In addition, areas of southern Africa, the Mediterranean, the Arctic, and Central America/western North America also emerge as prominent regional climate change hotspots in response to intermediate and high levels of forcing. Comparisons of different periods of the two forcing pathways suggest that the pattern of aggregate change is fairly robust to the level of global warming below approximately 2 °C of global warming (relative to the late-20th-century baseline), but not at the higher levels of global warming that occur in the late-21st-century period of the RCP8.5 pathway, with areas of southern Africa, the Mediterranean, and the Arctic exhibiting particular intensification of relative aggregate climate change in response to high levels of forcing. Although specific impacts will clearly be shaped by the interaction of climate change with human and biological vulnerabilities, our identification of climate change hotspots can help to inform mitigation and adaptation decisions by quantifying the rate, magnitude and causes of the aggregate climate response in different parts of the world.