70 research outputs found
African monsoon teleconnections with tropical SSTs: validation and evolution in a set of IPCC4 simulations
A set of 12 state-of-the-art coupled oceanatmosphere general circulation models (OAGCMs) is explored to assess their ability to simulate the main teleconnections between the West African monsoon (WAM) and the tropical sea surface temperatures (SSTs) at the interannual to multi-decadal time scales. Such teleconnections are indeed responsible for the main modes of precipitation variability observed over West Africa and represent an interesting benchmark for the models that have contributed to the fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC4). The evaluation is based on a maximum covariance analysis (MCA) applied on tropical SSTs and WAM rainfall. To distinguish between interannual and multi-decadal variability, all datasets are partitioned into low-frequency (LF) and high-frequency (HF) components prior to analysis. First applied to HF observations, the MCA reveals two major teleconnections. The first mode highlights the strong influence of the El Niño Southern Oscillation (ENSO). The second mode reveals a relationship between the SST in the Gulf of Guinea and the northward migration of the monsoon rainbelt over the West African continent. When applied to HF outputs of the twentieth century IPCC4 simulations, the MCA provides heterogeneous results. Most simulations show a single dominant Pacific teleconnection, which is, however, of the wrong sign for half of the models. Only one model shows a significant second mode, emphasizing the OAGCMsâ difficulty in simulating the response of the African rainbelt to Atlantic SST anomalies that are not synchronous with Pacific anomalies. The LF modulation of these HF teleconnections is then explored through running correlations between expansion coefficients (ECs) for SSTs and precipitation. The observed time series indicate that both Pacific and Atlantic teleconnections get stronger during the twentieth century. The IPCC4 simulations of the twentieth and twenty-first centuries do not show any significant change in the pattern of the teleconnections, but the dominant ENSO teleconnection also exhibits a significant strengthening, thereby suggesting that the observed trend could be partly a response to the anthropogenic forcing. Finally, the MCA is also applied to the LF data. The first observed mode reveals a well-known inter-hemispheric SST pattern that is strongly related to the multi-decadal variability of the WAM rainfall dominated by the severe drying trend from the 1950s to the 1980s. Whereas recent studies suggest that this drying could be partly caused by anthropogenic forcings, only 5 among the 12 IPCC4 models capture some features of this LF coupled mode. This result suggests the need for a more detailed validation of the WAM variability, including a dynamical interpretation of the SSTârainfall relationships
Role of wind stress in driving SST biases in the tropical Atlantic
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 six 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 south-eastern 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 south-eastern 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
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Cloud feedbacks in extratopical cyclones: insight from long-term satellite data and high-resolution global simulations
A negative extratropical shortwave cloud feedback driven by changes in cloud optical depth is a feature of global climate models (GCMs). A robust positive trend in observed liquid water path (LWP) over the last two decades across the warming Southern Ocean supports the negative shortwave cloud feedback predicted by GCMs. This feature has been proposed to be due to transitions from ice to liquid with warming. To gain insight into the shortwave cloud feedback we examine extratropical cyclone variability and the response of extratropical cyclones to transient warming in GCM simulations. Multi-Sensor Advanced Climatology Liquid Water Path (MAC-LWP) microwave observations of cyclone properties from the period 1992â2015 are contrasted with GCM simulations, with horizontal resolutions ranging from 7âkm to hundreds of kilometers. We find that inter-cyclone variability in LWP in both observations and models is strongly driven by the moisture flux along the cyclone's warm conveyor belt (WCB). Stronger WCB moisture flux enhances the LWP within cyclones. This relationship is replicated in GCMs, although its strength varies substantially across models. It is found that more than 80â% of the enhancement in Southern Hemisphere (SH) extratropical cyclone LWP in GCMs in response to a transient 4âK warming can be predicted based on the relationship between the WCB moisture flux and cyclone LWP in the historical climate and their change in moisture flux between the historical and warmed climates. Further, it is found that that the robust trend in cyclone LWP over the Southern Ocean in observations and GCMs is consistent with changes in the moisture flux. We propose two cloud feedbacks acting within extratropical cyclones: a negative feedback driven by ClausiusâClapeyron increasing water vapor path (WVP), which enhances the amount of water vapor available to be fluxed into the cyclone, and a feedback moderated by changes in the life cycle and vorticity of cyclones under warming, which changes the rate at which existing moisture is imported into the cyclone. Both terms contribute to increasing LWP within the cyclone. While changes in moisture flux predict cyclone LWP trends in the current climate and the majority of changes in LWP in transient warming simulations, a portion of the LWP increase in response to climate change that is unexplained by increasing moisture fluxes may be due to phase transitions. The variability in LWP within cyclone composites is examined to understand what cyclonic regimes the mixed-phase cloud feedback is relevant to. At a fixed WCB moisture flux cyclone LWP increases with increasing sea surface temperature (SST) in the half of the composite poleward of the low and decreases in the half equatorward of the low in both GCMs and observations. Cloud-top phase partitioning observed by the Atmospheric Infrared Sounder (AIRS) indicates that phase transitions may be driving increases in LWP in the poleward half of cyclones
Evaluating climate models with the CLIVAR 2020 ENSO Metrics Package
El NiñoâSouthern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts. It is therefore key to evaluate ENSO simulations in state-of-the-art numerical models used to study past, present, and future climate. Recently, the Pacific Region Panel of the International Climate and Ocean: Variability, Predictability and Change (CLIVAR) Project, as a part of the World Climate Research Programme (WCRP), led a community-wide effort to evaluate the simulation of ENSO variability, teleconnections, and processes in climate models. The new CLIVAR 2020 ENSO metrics package enables model diagnosis, comparison, and evaluation to 1) highlight aspects that need improvement; 2) monitor progress across model generations; 3) help in selecting models that are well suited for particular analyses; 4) reveal links between various model biases, illuminating the impacts of those biases on ENSO and its sensitivity to climate change; and to 5) advance ENSO literacy. By interfacing with existing model evaluation tools, the ENSO metrics package enables rapid analysis of multipetabyte databases of simulations, such as those generated by the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). The CMIP6 models are found to significantly outperform those from CMIP5 for 8 out of 24 ENSO-relevant metrics, with most CMIP6 models showing improved tropical Pacific seasonality and ENSO teleconnections. Only one ENSO metric is significantly degraded in CMIP6, namely, the coupling between the ocean surface and subsurface temperature anomalies, while the majority of metrics remain unchanged
An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part III: Hydrography and fluxes
In this paper we compare the simulated Arctic Ocean in 15 global oceanâsea ice models in the framework of the Coordinated Ocean-ice Reference Experiments, phase II (CORE-II). Most of these models are the ocean and sea-ice components of the coupled climate models used in the Coupled Model Intercomparison Project Phase 5 (CMIP5) experiments. We mainly focus on the hydrography of the Arctic interior, the state of Atlantic Water layer and heat and volume transports at the gateways of the Davis Strait, the Bering Strait, the Fram Strait and the Barents Sea Opening. We found that there is a large spread in temperature in the Arctic Ocean between the models, and generally large differences compared to the observed temperature at intermediate depths. Warm bias models have a strong temperature anomaly of inflow of the Atlantic Water entering the Arctic Ocean through the Fram Strait. Another process that is not represented accurately in the CORE-II models is the formation of cold and dense water, originating on the eastern shelves. In the cold bias models, excessive cold water forms in the Barents Sea and spreads into the Arctic Ocean through the St. Anna Through. There is a large spread in the simulated mean heat and volume transports through the Fram Strait and the Barents Sea Opening. The models agree more on the decadal variability, to a large degree dictated by the common atmospheric forcing. We conclude that the CORE-II model study helps us to understand the crucial biases in the Arctic Ocean. The current coarse resolution state-of-the-art ocean models need to be improved in accurate representation of the Atlantic Water inflow into the Arctic and density currents coming from the shelves
North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part I: Mean states
Simulation characteristics from eighteen global oceanâsea-ice coupled models are presented with a focus on the mean Atlantic meridional overturning circulation (AMOC) and other related fields in the North Atlantic. These experiments use inter-annually varying atmospheric forcing data sets for the 60-year period from 1948 to 2007 and are performed as contributions to the second phase of the Coordinated Ocean-ice Reference Experiments (CORE-II). The protocol for conducting such CORE-II experiments is summarized. Despite using the same atmospheric forcing, the solutions show significant differences. As most models also differ from available observations, biases in the Labrador Sea region in upper-ocean potential temperature and salinity distributions, mixed layer depths, and sea-ice cover are identified as contributors to differences in AMOC. These differences in the solutions do not suggest an obvious grouping of the models based on their ocean model lineage, their vertical coordinate representations, or surface salinity restoring strengths. Thus, the solution differences among the models are attributed primarily to use of different subgrid scale parameterizations and parameter choices as well as to differences in vertical and horizontal grid resolutions in the ocean models. Use of a wide variety of sea-ice models with diverse snow and sea-ice albedo treatments also contributes to these differences. Based on the diagnostics considered, the majority of the models appear suitable for use in studies involving the North Atlantic, but some models require dedicated development effort
River to ocean models interpolation
In CNRM-CM6-1 (Voldoire et al., 2019), the method used to interpolate river discharges simulated by the river routing CTRIP model to the NEMO ocean model is not conservative locally. This document explains the reasons of non-local conservation and proposes a new interpolation method that ensures local conservation. The consequences of this new interpolation are assessed in long term piControl type simulations in which forcing are fixed to preindustrial levels. In these simulations, we observe a strong impact on sea-ice extent and volume in both hemispheres. This in turn impacts the large-scale ocean mass transport (AMOC and ACC). Nevertheless, the resulting Arctic sea-ice extent is unrealistically large and it raises the need to tune the model ones the new interpolation method is included. More investigations would require such a tuning to be done. The new interpolation method can be applied to any other models given that the models ocean and river grids are dealt with in the OASIS coupler. This interpolation method could also be used for other quantities: biogenic fluxes, calving, etc. To this aim, it will be made available directly in OASIS future versions
ModĂšles climat: Plus dâun demi-siĂšcle de mĂ©canique des fluides numĂ©rique
International audienceThe first climate models have emerged in the 60s and have been continuously developed since then. They have progressively included the representation of all the climate system components: the atmosphere, the ocean, the cryosphere and the biosphere. Inside each component, they have also been enriched by the representation of more processes with the aim of improving their realism. These models are used to make climate projections over the coming century but they are above all a laboratory tool to improve our understanding of the climate system
Couplage océan-atmosphÚre-continent dans le systÚme climatique
Les modĂšles de climat constituent un laboratoire numĂ©rique pour mieux comprendre le systĂšme climatique et en proposer des projections. La modĂ©lisation du climat nĂ©cessite de reprĂ©senter toutes les composantes qui interagissent Ă ces Ă©chelles de temps : atmosphĂšre, ocĂ©an, biosphĂšre et cryosphĂšre. Le couplage de toutes ces composantes est nĂ©cessaire pour reprĂ©senter les flux de masse et d'Ă©nergie qui rĂ©gissent ces interactions. Les modĂšles sont imparfaits mais nous poursuivons constamment leur dĂ©veloppement en vue de les rendre le plus rĂ©aliste possible. Au fur et Ă mesure que nos connaissances progressent et que les capacitĂ©s des supercalculateurs sâaccroissent, les modĂšles se font plus prĂ©cis et reprĂ©sentent plus de processus.Mes activitĂ©s de recherche accompagnent lâĂ©volution du modĂšle de climat CNRM-CM, dĂ©veloppĂ© conjointement par le CNRM et le Cerfacs. Elles illustrent ce processus de dĂ©veloppement dâun modĂšle de climat sur deux dĂ©cennies. Dâune part, mes travaux ont contribuĂ© Ă lâamĂ©lioration de la modĂ©lisation du couplage entre les composantes du systĂšme climatique. Dâautre part, je mĂšne des Ă©tudes ciblĂ©es sur la reprĂ©sentation de processus couplĂ©s ocĂ©an-atmosphĂšre. Ces Ă©tudes ont un double objectif, mieux comprendre le laboratoire numĂ©rique pour guider les voies de dĂ©veloppement futures et ainsi concourir Ă son amĂ©lioration, mais aussi faire avancer nos connaissances sur les processus couplĂ©s qui rĂ©gissent le systĂšme climatique. Dans ce mĂ©moire, je retrace une partie de mes travaux en suivant ces deux axes : je prĂ©sente dans un premier chapitre certains de mes travaux qui ont contribuĂ© Ă lâamĂ©lioration du modĂšle et dans un deuxiĂšme chapitre, des Ă©tudes plus spĂ©cifiques qui ont permis de faire avancer notre comprĂ©hension des processus couplĂ©s qui rĂ©gissent le systĂšme climatique. Dans cette deuxiĂšme partie, je mets plus particuliĂšrement en avant les travaux rĂ©alisĂ©s via lâencadrement dâĂ©tudiants (stages, thĂšses et post-doctorats).La communautĂ© des modĂ©lisateurs du climat fait le constat quâil est de plus en plus difficile dâaccroĂźtre les performances des modĂšles, dâune part parce que lâaccroissement de la complexitĂ© ne les rend pas forcĂ©ment plus rĂ©alistes, mais aussi parce que notre marge de progression semble plus tĂ©nue. Dâailleurs, que veut dire amĂ©liorer leurs performances ? Cherche-t-on Ă dĂ©velopper un modĂšle universel qui permette dâĂ©tudier tous les processus climatiques ou bien devons-nous dĂ©velopper autant de modĂšles que dâapplications ? Pour progresser, notre communautĂ© se retrouve face Ă de nouveaux dĂ©fis quâelle tente de relever. Pour ma part, je propose de revenir sur la mĂ©thode de calibration des modĂšles. En effet, la calibration a Ă©tĂ©, jusque lĂ , rĂ©alisĂ©e de façon heuristique et est peu documentĂ©e. AmĂ©liorer cette Ă©tape en dĂ©veloppant une mĂ©thode objective permettra de renforcer notre confiance dans les modĂšles, de mieux qualifier leurs incertitudes paramĂ©triques mais Ă©galement dâaffiner notre mise en Ćuvre de modĂšles Ă plus haute rĂ©solution
Couplage océan-atmosphÚre-continent dans le systÚme climatique
Les modĂšles de climat constituent un laboratoire numĂ©rique pour mieux comprendre le systĂšme climatique et en proposer des projections. La modĂ©lisation du climat nĂ©cessite de reprĂ©senter toutes les composantes qui interagissent Ă ces Ă©chelles de temps : atmosphĂšre, ocĂ©an, biosphĂšre et cryosphĂšre. Le couplage de toutes ces composantes est nĂ©cessaire pour reprĂ©senter les flux de masse et d'Ă©nergie qui rĂ©gissent ces interactions. Les modĂšles sont imparfaits mais nous poursuivons constamment leur dĂ©veloppement en vue de les rendre le plus rĂ©aliste possible. Au fur et Ă mesure que nos connaissances progressent et que les capacitĂ©s des supercalculateurs sâaccroissent, les modĂšles se font plus prĂ©cis et reprĂ©sentent plus de processus.Mes activitĂ©s de recherche accompagnent lâĂ©volution du modĂšle de climat CNRM-CM, dĂ©veloppĂ© conjointement par le CNRM et le Cerfacs. Elles illustrent ce processus de dĂ©veloppement dâun modĂšle de climat sur deux dĂ©cennies. Dâune part, mes travaux ont contribuĂ© Ă lâamĂ©lioration de la modĂ©lisation du couplage entre les composantes du systĂšme climatique. Dâautre part, je mĂšne des Ă©tudes ciblĂ©es sur la reprĂ©sentation de processus couplĂ©s ocĂ©an-atmosphĂšre. Ces Ă©tudes ont un double objectif, mieux comprendre le laboratoire numĂ©rique pour guider les voies de dĂ©veloppement futures et ainsi concourir Ă son amĂ©lioration, mais aussi faire avancer nos connaissances sur les processus couplĂ©s qui rĂ©gissent le systĂšme climatique. Dans ce mĂ©moire, je retrace une partie de mes travaux en suivant ces deux axes : je prĂ©sente dans un premier chapitre certains de mes travaux qui ont contribuĂ© Ă lâamĂ©lioration du modĂšle et dans un deuxiĂšme chapitre, des Ă©tudes plus spĂ©cifiques qui ont permis de faire avancer notre comprĂ©hension des processus couplĂ©s qui rĂ©gissent le systĂšme climatique. Dans cette deuxiĂšme partie, je mets plus particuliĂšrement en avant les travaux rĂ©alisĂ©s via lâencadrement dâĂ©tudiants (stages, thĂšses et post-doctorats).La communautĂ© des modĂ©lisateurs du climat fait le constat quâil est de plus en plus difficile dâaccroĂźtre les performances des modĂšles, dâune part parce que lâaccroissement de la complexitĂ© ne les rend pas forcĂ©ment plus rĂ©alistes, mais aussi parce que notre marge de progression semble plus tĂ©nue. Dâailleurs, que veut dire amĂ©liorer leurs performances ? Cherche-t-on Ă dĂ©velopper un modĂšle universel qui permette dâĂ©tudier tous les processus climatiques ou bien devons-nous dĂ©velopper autant de modĂšles que dâapplications ? Pour progresser, notre communautĂ© se retrouve face Ă de nouveaux dĂ©fis quâelle tente de relever. Pour ma part, je propose de revenir sur la mĂ©thode de calibration des modĂšles. En effet, la calibration a Ă©tĂ©, jusque lĂ , rĂ©alisĂ©e de façon heuristique et est peu documentĂ©e. AmĂ©liorer cette Ă©tape en dĂ©veloppant une mĂ©thode objective permettra de renforcer notre confiance dans les modĂšles, de mieux qualifier leurs incertitudes paramĂ©triques mais Ă©galement dâaffiner notre mise en Ćuvre de modĂšles Ă plus haute rĂ©solution
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