Decadal predictability of the Atlantic meridional overturning circulation and climate in the IPSL-CM5A-LR model

This study explores the decadal potential predictability of the Atlantic Meridional Overturning Circulation (AMOC) as represented in the IPSL-CM5A-LR model, along with the predictability of associated oceanic and atmospheric fields. Using a 1000-year control run, we analyze the prognostic potential predictability (PPP) of the AMOC through ensembles of simulations with perturbed initial conditions. Based on a measure of the ensemble spread, the modelled AMOC has an average predictive skill of 8 years, with some degree of dependence on the AMOC initial state. Diagnostic potential predictability of surface temperature and precipitation is also identified in the control run and compared to the PPP. Both approaches clearly bring out the same regions exhibiting the highest predictive skill. Generally, surface temperature has the highest skill up to 2 decades in the far North Atlantic ocean. There are also weak signals over a few oceanic areas in the tropics and subtropics. Predictability over land is restricted to the coastal areas bordering oceanic predictable regions. Potential predictability at interannual and longer timescales is largely absent for precipitation in spite of weak signals identified mainly in the Nordic Seas. Regions of weak signals show some dependence on AMOC initial state. All the identified regions are closely linked to decadal AMOC fluctuations suggesting that the potential predictability of climate arises from the mechanisms controlling these fluctuations. Evidence for dependence on AMOC initial state also suggests that studying skills from case studies may prove more useful to understand predictability mechanisms than computing average skill from numerous start dates.     

Climate predictability on interannual to decadal time scales: the initial value problem

Any initial value forecast of climate will be subject to errors originating from poorly known initial conditions, model imperfections, and by "chaos" in the sense that, even if the initial conditions were perfectly known, infinitesimal errors can amplify and spoil the forecast at some lead time. Here the latter source of error is examined using a "perfect model" approach whereby small perturbations are made to a coupled atmosphere-ocean general circulation model and the spread of nearby model trajectories, on time and space scales appropriate to seasonal-decadal climate variability, is measured to assess the lead time at which the error saturates. The study therefore represents an estimate of the upper limit of the predictability of climate (appropriate to the initial value problem) given a perfect model and near perfect knowledge of the initial conditions. It is found that, on average, surface air temperature anomalies are potentially predictable on seasonal to interannual time scales in the tropical regions and are potentially predictable on decadal time scales over the ocean in the North Atlantic. For mid-latitude surface air temperature anomalies over land, model trajectories rapidly diverge and there is little sign of any potential predictability on time scales greater than a season or so. For mean sea level pressure anomalies, there is potential predictability on seasonal time scales in the tropics, and for some global scale annual-decadal anomalies, although not those associated with the North Atlantic Oscillation. For precipitation, the only potential for predictability is for seasonal time anomalies associated with the El-Niño Southern Oscillation. For the majority of the highly populated regions of the world, climate predictability on interannual to decadal time scales based in the initial value approach is likely to be severely limited by chaotic error growth. It is found however that there can be cases in which the potential predictability can be higher than average indicating that there is perhaps some utility in making initial value forecasts of climate in those regions which show low predictability on average.     

A predictability study of simulated North Atlantic multidecadal variability

 The North Atlantic is one of the few places on the globe where the atmosphere is linked to the deep ocean through air–sea interaction. While the internal variability of the atmosphere by itself is only predictable over a period of one to two weeks, climate variations are potentially predictable for much longer periods of months or even years because of coupling with the ocean. This work presents details from the first study to quantify the predictability for simulated multidecadal climate variability over the North Atlantic. The model used for this purpose is the GFDL coupled ocean-atmosphere climate model used extensively for studies of global warming and natural climate variability. This model contains fluctuations of the North Atlantic and high-latitude oceanic circulation with variability concentrated in the 40–60 year range. Oceanic predictability is quantified through analysis of the time-dependent behavior of large-scale empirical orthogonal function (EOF) patterns for the meridional stream function, dynamic topography, 170 m temperature, surface temperature and surface salinity. The results indicate that predictability in the North Atlantic depends on three main physical mechanisms. The first involves the oceanic deep convection in the subpolar region which acts to integrate atmospheric fluctuations, thus providing for a red noise oceanic response as elaborated by Hasselmann. The second involves the large-scale dynamics of the thermohaline circulation, which can cause the oceanic variations to have an oscillatory character on the multidecadal time scale. The third involves nonlocal effects on the North Atlantic arising from periodic anomalous fresh water transport advecting southward from the polar regions in the East Greenland Current. When the multidecadal oscillatory variations of the thermohaline circulation are active, the first and second EOF patterns for the North Atlantic dynamic topography have predictability time scales on the order of 10–20 y, whereas EOF-1 of SST has predictability time scales of 5–7 y. When the thermohaline variability has weak multidecadal power, the Hasselmann mechanism is dominant and the predictability is reduced by at least a factor of two. When the third mechanism is in an extreme phase, the North Atlantic dynamic topography patterns realize a 10–20 year predictability time scale. Additional analysis of SST in the Greenland Sea, in a region associated with the southward propagating fresh water anomalies, indicates the potential for decadal scale predictability for this high latitude region as well. The model calculations also allow insight into regional variations of predictability, which might be useful information for the design of a monitoring system for the North Atlantic. Predictability appears to break down most rapidly in regions of active convection in the high-latitude regions of the North Atlantic. Received: 28 October 1996 / Accepted: 21 March 1997     

一个斜压海洋风生环流的数值模拟

为研究大尺度海气相互作用、气候以及气候的变化,一个具有规则海岸线和无海底地形的三层斜压海洋模式已经建立起来。积分区域包括了赤道地区在内,南北方向从-2750 km到+4750 km,东西方向宽10000 km,总深度是4 km。模式方程为原始方程,用了静力近似和Bou-sinesq近似。模式数值积分分两个阶段,在第一个阶段,海洋环流从静止状态开始,以只随纬度变化的年平均风应力和海表热通量作为强迫边条件数值积分十年,这时海洋上层的环流和温度都趋于平稳,我们将第十年末的积分状态作为模式气候的准平衡态。计算结果模拟了热带太平洋上气候平均的温度分布以及主要海流的大尺度特征,如赤道地区的狭长冷水带和强上翻区、南赤道洋流、北赤道逆流以及表层下面沿赤道自西向东的潜流。在第二个阶段,以在第一个阶段末尾得到的准平衡态作为初值,分别用冬季和夏季的风应力作为强迫边条件再积分一年。比较这两种计算结果,我们看到海洋环流随信风系统的季节变化也呈现出明显的季节变化,特别是北赤道逆流,其强度在夏季强,在冬季弱甚至不出现。南赤道洋流无论哪个季节都穿过赤道,在赤道以北的南赤道洋流冬季比夏季强,而在赤道以南的南赤道洋流夏季比冬季强。     

Predictability of Mediterranean climate variables from oceanic variability. Part II: Statistical models for monthly precipitation and temperature in the Mediterranean area

The objective of this study is to investigate the predictability of monthly climate variables in the Mediterranean area by using statistical models. It is a well-known fact that the future state of the atmosphere is sensitive to preceding conditions of the slowly varying ocean component with lead times being sufficiently long for predictive assessments. Sea surface temperatures (SSTs) are therefore regarded as one of the best variables to be used in seasonal climate predictions. In the present study, SST-regimes which have been derived and discussed in detail in Part I of this paper, are used with regard to monthly climate predictions for the Mediterranean area. Thus, cross-correlations with time lags from 0 up to 12?months and ensuing multiple regression analyses between the large-scale SST-regimes and monthly precipitation and temperature for Mediterranean sub-regions have been performed for the period 1950?C2003. Statistical hindcast ensembles of Mediterranean precipitation including categorical forecast skill can be identified only for some months in different seasons and for some individual regions of the Mediterranean area. Major predictors are the tropical Atlantic Ocean and the North Atlantic Ocean SST-regimes, but significant relationships can also be found with tropical Pacific and North Pacific SST-regimes. Statistical hindcast ensembles of Mediterranean temperature with some categorical forecast skill can be determined primarily for the Western Mediterranean and the North African regions throughout the year. As for precipitation the major predictors for temperature are located in the tropical Atlantic Ocean and the North Atlantic Ocean, but some connections also exist with the Pacific SST variations.     

Interannual-to-decadal variability of the North Atlantic from an ocean data assimilation system

An ocean analysis, assimilating both surface and subsurface hydrographic temperature data into a global ocean model, has been produced for the period 1958–2000, and used to study the time and space variations of North Atlantic upper ocean heat content (HC). Observational evidence is presented for interannual-to-decadal variability of upper ocean thermal fluctuations in the North Atlantic related to the North Atlantic Oscillation (NAO) variability over the last 40 years. The assimilation scheme used in the ocean analysis is a univariate, variational optimum interpolation of temperature. The first guess is produced by an eddy permitting global ocean general circulation forced by atmospheric reanalysis from the National Center for Environmental Prediction (NCEP). The validation of the ocean analysis has been done through the comparison with objectively analyzed observations and independent data sets. The method is able to compensate for the model systematic error to reproduce a realistic vertical thermal structure of the region and to improve consistently the model estimation of the time variability of the upper ocean temperature. Empirical orthogonal function (EOF) analysis shows that an important mode of variability of the wintertime upper ocean climate over the North Atlantic during the period of study is characterized by a tripole pattern both for SST and upper ocean HC. A similar mode is found for summer HC anomalies but not for summer SST. Over the whole period, HC variations in the subtropics show a general warming trend while the tropical and north eastern part of the basin have an opposite cooling tendency. Superimposed on this linear trend, the HC variability explained by the first EOF both in winter and summer conditions reveals quasi-decadal oscillations correlated with changes in the NAO index. On the other hand, there is no evidence of correlation in time between the NAO index and the upper ocean HC averaged over the whole North Atlantic which exhibits a substantial and monotonic warming trend during the last two decades of the analysis period. The maximum correlation is found between the leading principal component of winter HC anomalies and NAO index at 1 year lag with NAO leading. For SST anomalies significant correlation is found only for winter conditions. In contrast, for HC anomalies high correlations are found also in the summer suggesting that the summer HC keeps a memory of winter conditions.     

Predictability of the subtropical dipole modes in a coupled ocean–atmosphere model

Predictability of the subtropical dipole modes is assessed using the SINTEX-F coupled model. Despite the known difficulty in predicting subtropical climate due to large internal variability of the atmosphere and weak ocean–atmosphere coupling, it is shown for the first time that the coupled model can successfully predict the South Atlantic Subtropical Dipole (SASD) 1 season ahead, and the prediction skill is better than the persistence in all the 1–12 month lead hindcast experiments. There is a prediction barrier in austral winter due to the seasonal phase locking of the SASD to austral summer. The prediction skill is lower for the Indian Ocean Subtropical Dipole (IOSD) than for the SASD, and only slightly better than the persistence till 6-month lead because of the low predictability of the sea surface temperature anomaly in its southwestern pole. However, for some strong IOSD events in the last three decades, the model can predict them 1 season ahead. The co-occurrence of the negative SASD and IOSD in 1997/1998 austral summer can be predicted from July 1st of 1997. This is because the negative sea level pressure anomalies over the South Atlantic and the southern Indian Ocean in September–October (November–December) that trigger the occurrence of the negative SASD and IOSD are related to the well predicted tropical Indian Ocean Dipole (El Niño/Southern Oscillation). Owing to the overall good performances of the SINTEX-F model in predicting the SASD, some strong IOSD, and El Niño/Southern Oscillation, the prediction skill of the southern African summer precipitation is high in the SINTEX-F model.     

Potential predictability of the Asian summer monsoon on monthly and seasonal time scales

Summary ¶The potential predictability of the monthly and seasonal means during the Northern Hemisphere summer and winter is studied by estimating the signal-to-noise ratio. Based on 33 years of daily low-level wind observations and 24 years of satellite observations of outgoing long wave radiation, the predictability of the Asian summer monsoon region is contrasted with that over other tropical regions. A method of separating the contributions from slowly varying boundary forcing and internal dynamics (e.g., intraseasonal oscillations) that determine the predictability of the monthly mean tropical climate is proposed. We show that the Indian monsoon climate is only marginally predictable in monthly time scales as the contribution of the boundary forcing in this region is relatively low and that of the internal dynamics is relatively large. It is shown that excluding the Indian monsoon region, the predictable region is larger and predictability is higher in the tropics during northern summer. Even though the boundary forced variance is large during northern winter, the predictable region is smaller as the internal variance is larger and covers a larger region during northern winter (due to stronger intraseasonal activity). Consistent with the estimates of predictability of monthly means, estimates of potential predictability on seasonal time scales also indicate that predictability of seasonal mean Indian monsoon is limited.Received December 6, 2002; accepted March 16, 2003 Published online: June 12, 2003     

北印度洋气候对冬夏季航线选择的影响

文章介绍了海洋气象导航发展的必然性及其气象航线选择的原理和影响因素。结合中央气象台海洋气象导航中心近10年来的实船导航业务，分析了北印度洋气候对冬、夏季航线选择的影响因素，指出应结合北印度洋的冬、夏季气候变化及地形特点选择不同的气象航线。该文为在实际工作中根据不同的季节及船型情况选择不同的航线、规避大风和巨浪出现频率高的区域，以及保持良好的航行条件提供了依据。     

Atmospheric bridge on orbital time scales

Large-scale atmospheric patterns are examined on orbital timescales using a climate model which explicitly resolves the atmosphere–ocean–sea ice dynamics. It is shown that, in contrast to boreal summer where the climate mainly follows the local radiative forcing, the boreal winter climate is strongly determined by modulation of circulation modes linked to the Arctic Oscillation/North Atlantic Oscillation (AO/NAO) and the Northern/Southern Annular Modes. We find that during a positive phase of the AO/NAO the convection in the tropical Pacific is below normal. The related atmospheric circulation provides an atmospheric bridge for the precessional forcing inducing a non-uniform temperature anomalies with large amplitudes over the continents. We argue that this is important for mechanisms responsible for multi-millennial climate variability and glacial inception.     

Influence of ENSO and the South Atlantic Ocean on climate predictability over Southeastern South America

We perform a systematic study of the predictability of surface air temperature and precipitation in Southeastern South America (SESA) using ensembles of AGCM simulations, focusing on the role of the South Atlantic and its interaction with the El Niño-Southern Oscillation (ENSO). It is found that the interannual predictability of climate over SESA is strongly tied to ENSO showing high predictability during the seasons and periods when there is ENSO influence. The most robust ENSO signal during the whole period of study (1949–2006) is during spring when warm events tend to increase the precipitation over Southeastern South America. Moreover, the predictability shows large inter-decadal changes: for the period 1949–1977, the surface temperature shows high predictability during late fall and early winter. On the other hand, for the period 1978–2006, the temperature shows (low) predictability only during winter, while the precipitation shows not only high predictability in spring but also in fall. Furthermore, it is found that the Atlantic does not directly affect the climate over SESA. However, the experiments where air–sea coupling is allowed in the south Atlantic suggest that this ocean can act as a moderator of the ENSO influence. During warm ENSO events the ocean off Brazil and Uruguay tends to warm up through changes in the atmospheric heat fluxes, altering the atmospheric anomalies and the predictability of climate over SESA. The main effect of the air–sea coupling is to strengthen the surface temperature anomalies over SESA; changes in precipitation are more subtle. We further found that the thermodynamic coupling can increase or decrease the predictability. For example, the air–sea coupling significantly increases the skill of the model in simulating the surface air temperature anomalies for most seasons during period 1949–1977, but tends to decrease the skill in late fall during period 1978–2006. This decrease in skill during late fall in 1978–2006 is found to be due to a wrong simulation of the remote ENSO signal that is further intensified by the local air–sea coupling in the south Atlantic. Thus, our results suggest that climate models used for seasonal prediction should simulate correctly not only the remote ENSO signal, but also the local air–sea thermodynamic coupling.     

Statistical Analysis and Prediction of KwaZulu-Natal Climate

Summary. Climatic fluctuations in KwaZulu-Natal, southeastern South Africa, are analysed using statistical techniques. Moist easterly winds sweep in from the Indian Ocean during all seasons except winter, producing a balance between evaporative losses and precipitation. The seasonal cycle is unimodal with a peak of rainfall and temperature in the summer months (December to February) with a 1–2 month lag for streamflow and vegetation growth. Rainfall and temperature departures in recent decades exhibit a 3 year cycle and a 3–6 month persistence of cool/wet or warm/dry phases. The predictability of summer rainfall, temperature, crop yield, inflow to dams and malaria incidence is explored. Multivariate linear regression models with lead-times of one season account for two-thirds of the variance in most cases. Climatic signals which enable predictability include winds over the tropical east Atlantic and north Indian Ocean. El Ni?o signals from tropical Pacific sea surface temperatures and the Southern Oscillation Index are also important predictors for KwaZulu-Natal’s climate. These relationships suggest that local circulation responses to large scale tropical-polar temperature gradients govern climatic fluctuations over KwaZulu-Natal. Received August 27, 1997. Revised November 10, 1997     

Interannual to decadal summer drought variability over Europe and its relationship to global sea surface temperature

Interannual to decadal variability of European summer drought and its relationship with global sea surface temperature (SST) is investigated using the newly developed self calibrated Palmer drought severity index (scPDSI) and global sea surface temperature (SST) field for the period 1901–2002. A European drought severity index defined as the average of scPDSI over entire Europe shows quasiperiodic variations in the 2.5–5 year band as well as at 12–13 years suggesting a possible potential predictability of averaged drought conditions over Europe. A Canonical Correlation Analysis between summer scPDSI anomalies over Europe and global SST anomalies reveals the existence of three modes of coupled summer drought scPDSI patterns and winter global SST anomalies. The first scPDSI-SST coupled mode represents the long-term trends in the data which manifest in SST as warming over all oceans. The associated long-term trend in scPDSI suggests increasing drought conditions over the central part of Europe. The second mode is related to the inter-annual ENSO and decadal PDO influence on the European climate and the third one captures mainly the drought pattern associated to Atlantic Multidecadal Oscillation. The lag relationships between winter SST and summer drought conditions established in this study can provide a valuable skill for the prediction of drought conditions over Europe on interannual to decadal time scales.     

The internal climate variability of HadCM3, a version of the Hadley Centre coupled model without flux adjustments

We examine the internal climate variability of a 1000?year long integration of the third version of the Hadley Centre coupled model (HadCM3). The model requires no flux adjustment, needs no spin up procedure prior to coupling and has a stable climate in the global mean. The principal aims are (1) to validate the internal climate variability against observed climate variability, (2) to examine the model for any periodic modes of variability, (3) to use the model estimate of internal climate variability to asses the probability of occurrence of observed trends in climate variables, and (4) to compare HadCM3 with the previous version of the Hadley Centre model, HadCM2. The magnitude and frequency characteristics of the variability of the global mean surface temperature of HadCM3 on annual to decadal time scales is in good agreement with the observations. Observed upward trends in temperature over the last 20?years and longer are inconsistent with the internal variability of the model. The simulated spatial pattern of surface temperature variability is qualitatively similar to that observed, although there is an overestimation of the land temperature variability and regional errors in ocean temperature variability. The model simulates an El Niño Southern Oscillation with an irregular 3–4?year cycle, and with a teleconnection pattern which is much more like the observations than was found in HadCM2. The interdecadal variability of the model ocean in the tropical Pacific, North Pacific and North Atlantic is broadly similar to that in the real world with none of the simulated patterns having any periodic behaviour. HadCM3 simulates an Arctic Oscillation/North Atlantic Oscillation (NAO) in Northern Hemisphere winter which has a spatial pattern consistent with the observations in the Atlantic region, but has too much teleconnection with the North Pacific. The recent observed upward trend in the NAO index is inconsistent with the model internal variability. The variability of the simulated zonal mean atmospheric temperature shows some marked differences to the observed zonal mean temperature variability, although the comparison is confounded by the sparse observational network and its possible contamination by a climate change signal.     

Interannual variability of CFC-11 absorption by the ocean: an offline model study

The global ocean Chlorofluorocarbon (CFC-11) was simulated in an offline model driven by re-analysis ocean currents in order to identify the mechanisms of interannual to interdecadal variability of air?Csea CFC fluxes. The model was forced with the observed anthropogenic perturbations of atmospheric CFC-11 from the post industrial period (1938) following the OCMIP-II flux protocols along with the observed winds from 1960 to 1999 in the formulation of surface gas exchanges. The model ocean CFC-11 inventories, at the end of 1990s, accounted approximately 1% of the total atmospheric CFC-11, which is consistent with the corresponding observations. The mid-to-high latitude oceans were venue for strong (weak) oceanic sinks (sources) of CFC-11 during the winter (summer) months. The Southern Ocean (south of 40°S) and the North Atlantic (north of 35°N) provided two largest sinks of CFC-11, through which 31.4 and 14.6% of the global ocean CFC-11 entered, respectively. The eastern tropical Pacific Ocean exhibited large interannual variability of CFC-11 flux with a strong (weak) sink during La Ni?a (El Ni?o) years and represented 36% of the global CFC-11 flux variability. The North Atlantic and Southern Ocean were found as regions of large sink efficiency: a capacity to sink more CFC than outsource, although it reduced by 80 and 70%, respectively, in the last 40?years compared to 1960. The sink to source ratio of global ocean CFC-11 fluxes were reduced from 90 to 50% in the last 40?years. This indicates a saturation of CFC in the above-thermocline subsurface that makes the upper ocean less efficient in absorbing CFC in recent decades. A positive trend in CFC sink is now limited to the Southern Ocean, central tropical Pacific and western boundary current regions which possess active upwelling of old water with long time since last atmospheric contact. However, a globally averaged trend was a reduced CFC-11 sink, by emitting 30% of the total ocean CFC-11 that was absorbed during last 40?years.     

Extremes and predictability in the European pre-industrial climate of a regional climate model

A high-resolution pre-industrial control simulation with the regional climate model REMO is analyzed in detail for different European subregions. To our knowledge, this is the first long pre-industrial control simulation by a regional climate model as well as at comparable resolution. We assess the ability of the climate model to reproduce the observed climate variability in various parts of the continent. In order to investigate the representation of extreme events in the model under pre-industrial greenhouse gas concentrations, selected seasons are examined with regard to the atmospheric circulation and other climatic characteristics that have contributed to the occurrences. A special focus is dedicated to land-atmosphere interactions. Extreme seasons are simulated by the model under various circumstances, some of them strongly resemble observed periods of extraordinary conditions like the summer 2003 or autumn 2006 in parts of Europe. The regional perspective turns out to be of importance when analyzing events that are constituted by meso-scale atmospheric dynamics. Moreover, the predictability of the European climate on seasonal to decadal time scales is examined by relating the statistics of surface variables to large-scale modes of variability impacting the North Atlantic sector like the Meridional Overturning Circulation, the El Niño Southern Oscillation, and the North Atlantic Oscillation. For this purpose, we introduce a measure of tail dependence that quantifies the correlation between extreme values in two variables that describe the state of the climate system. Significant dependence of extreme events can be detected in various situations.     

大气再分析资料中潜在可预报性的特征及其差异

利用1957年9月—2002年8月ECMWF和NCEP/NCAR月平均再分析资料,分别讨论了冬季和夏季SLP(sea level pressure,海平面气压)、500hPa高度、200hPa纬向风和850hPa经向风年际变率的潜在可预报性特征。结果表明:热带地区的潜在可预报性较高,尤其是赤道中东太平洋地区,而中高纬地区的潜在可预报性则较低。比较两套资料潜在可预报性的异同后发现:南半球的差异均明显大于北半球,特别是南极地区;低层变量的差异均大于中高层变量;东亚大陆在冬、夏季均具有一定的潜在可预报性;冬季各变量均表现出东亚冬季风具有较高的年际变率潜在可预报性,且两套资料的差异较小;500hPa位势高度表现的东亚夏季风潜在可预报性在两套资料中较一致,而低层变量(SLP和850hPa经向风)表现的东亚夏季风年际变率潜在可预报性在两套资料中存在较大差异。     

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.     

Interannual predictability of Arctic sea ice in a global climate model: regional contrasts and temporal evolution

The predictability of the Arctic sea ice is investigated at the interannual time scale using decadal experiments performed within the framework of the fifth phase of the Coupled Model Intercomparison Project with the CNRM-CM5.1 coupled atmosphere–ocean global climate model. The predictability of summer Arctic sea ice extent is found to be weak and not to exceed 2 years. In contrast, robust prognostic potential predictability (PPP) up to several years is found for winter sea ice extent and volume. This predictability is regionally contrasted. The marginal seas in the Atlantic sector and the central Arctic show the highest potential predictability, while the marginal seas in the Pacific sector are barely predictable. The PPP is shown to decrease drastically in the more recent period. Regarding sea ice extent, this decrease is explained by a strong reduction of its natural variability in the Greenland–Iceland–Norwegian Seas due to the quasi-disappearance of the marginal ice zone in the center of the Greenland Sea. In contrast, the decrease of predictability of sea ice volume arises from the combined effect of a reduction of its natural variability and an increase in its chaotic nature. The latter is attributed to a thinning of sea ice cover over the whole Arctic, making it more sensitive to atmospheric fluctuations. In contrast to the PPP assessment, the prediction skill as measured by the anomaly correlation coefficient is found to be mostly due to external forcing. Yet, in agreement with the PPP assessment, a weak added value of the initialization is found in the Atlantic sector. Nevertheless, the trend-independent component of this skill is not statistically significant beyond the forecast range of 3 months. These contrasted findings regarding potential predictability and prediction skill arising from the initialization suggest that substantial improvements can be made in order to enhance the prediction skill.     

Impact of initialization procedures on the predictive skill of a coupled ocean–atmosphere model

The sensitivity of the predictive skill of a decadal climate prediction system is investigated with respect to details of the initialization procedure. For this purpose, the coupled ocean–atmosphere UCLA/MITgcm climate model is initialized using the following three different initialization approaches: full state initialization (FSI), anomaly initialization (AI) and FSI employing heat flux and freshwater flux corrections (FC). The ocean initial conditions are provided by the German contribution to Estimating the Circulation and Climate of the Ocean state estimate (GECCO project), from which ensembles of decadal hindcasts are initialized every 5 years from 1961 to 2001. The predictive skill for sea surface temperature (SST), sea surface height (SSH) and the Atlantic meridional overturning circulation (AMOC) is assessed against the GECCO synthesis. In regions with a deep mixed layer the predictive skill for SST anomalies remains significant for up to a decade in the FC experiment. By contrast, FSI shows less persistent skill in the North Atlantic and AI does not show high skill in the extratropical Southern Hemisphere, but appears to be more skillful in the tropics. In the extratropics, the improved skill is related to the ability of the FC initialization method to better represent the mixed layer depth, and the highest skill occurs during wintertime. The correlation skill for the spatially averaged North Atlantic SSH hindcasts remains significant up to a decade only for FC. The North Atlantic MOC initialized hindcasts show high correlation values in the first pentad while correlation remains significant in the following pentad too for FSI and FC. Overall, for the current setup, the FC approach appears to lead to the best results, followed by the FSI and AI procedures.