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 OAGCMs’ 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 SST–rainfall 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

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 1992–2015 are contrasted with GCM simulations, with horizontal resolutions ranging from 7 km 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 80 % of the enhancement in Southern Hemisphere (SH) extratropical cyclone LWP in GCMs in response to a transient 4 K 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 Clausius–Clapeyron 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 Niño–Southern 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 ocean–sea 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 ocean–sea-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

Modèles climat: Plus d’un demi-siècle de mécanique des fluides numérique

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-atmosphère-continent dans le système climatique

Les modèles de climat constituent un laboratoire numérique pour mieux comprendre le système climatique et en proposer des projections. La modélisation du climat nécessite de représenter toutes les composantes qui interagissent à ces échelles de temps : atmosphère, océan, biosphère et cryosphère. Le couplage de toutes ces composantes est nécessaire pour représenter les flux de masse et d'énergie qui régissent ces interactions. Les modèles sont imparfaits mais nous poursuivons constamment leur développement en vue de les rendre le plus réaliste possible. Au fur et à mesure que nos connaissances progressent et que les capacités des supercalculateurs s’accroissent, les modèles se font plus précis et représentent plus de processus.Mes activités de recherche accompagnent l’évolution du modèle de climat CNRM-CM, développé conjointement par le CNRM et le Cerfacs. Elles illustrent ce processus de développement d’un modèle de climat sur deux décennies. D’une part, mes travaux ont contribué à l’amélioration de la modélisation du couplage entre les composantes du système climatique. D’autre part, je mène des études ciblées sur la représentation de processus couplés océan-atmosphère. Ces études ont un double objectif, mieux comprendre le laboratoire numérique pour guider les voies de développement futures et ainsi concourir à son amélioration, mais aussi faire avancer nos connaissances sur les processus couplés qui régissent le système climatique. Dans ce mémoire, je retrace une partie de mes travaux en suivant ces deux axes : je présente dans un premier chapitre certains de mes travaux qui ont contribué à l’amélioration du modèle et dans un deuxième chapitre, des études plus spécifiques qui ont permis de faire avancer notre compréhension des processus couplés qui régissent le système climatique. Dans cette deuxième partie, je mets plus particulièrement en avant les travaux réalisés via l’encadrement d’étudiants (stages, thèses et post-doctorats).La communauté des modélisateurs du climat fait le constat qu’il est de plus en plus difficile d’accroître les performances des modèles, d’une part parce que l’accroissement de la complexité ne les rend pas forcément plus réalistes, mais aussi parce que notre marge de progression semble plus ténue. D’ailleurs, que veut dire améliorer leurs performances ? Cherche-t-on à développer un modèle universel qui permette d’étudier tous les processus climatiques ou bien devons-nous développer autant de modèles que d’applications ? Pour progresser, notre communauté se retrouve face à de nouveaux défis qu’elle tente de relever. Pour ma part, je propose de revenir sur la méthode de calibration des modèles. En effet, la calibration a été, jusque là, réalisée de façon heuristique et est peu documentée. Améliorer cette étape en développant une méthode objective permettra de renforcer notre confiance dans les modèles, de mieux qualifier leurs incertitudes paramétriques mais également d’affiner notre mise en œuvre de modèles à plus haute résolution

Couplage océan-atmosphère-continent dans le système climatique

Les modèles de climat constituent un laboratoire numérique pour mieux comprendre le système climatique et en proposer des projections. La modélisation du climat nécessite de représenter toutes les composantes qui interagissent à ces échelles de temps : atmosphère, océan, biosphère et cryosphère. Le couplage de toutes ces composantes est nécessaire pour représenter les flux de masse et d'énergie qui régissent ces interactions. Les modèles sont imparfaits mais nous poursuivons constamment leur développement en vue de les rendre le plus réaliste possible. Au fur et à mesure que nos connaissances progressent et que les capacités des supercalculateurs s’accroissent, les modèles se font plus précis et représentent plus de processus.Mes activités de recherche accompagnent l’évolution du modèle de climat CNRM-CM, développé conjointement par le CNRM et le Cerfacs. Elles illustrent ce processus de développement d’un modèle de climat sur deux décennies. D’une part, mes travaux ont contribué à l’amélioration de la modélisation du couplage entre les composantes du système climatique. D’autre part, je mène des études ciblées sur la représentation de processus couplés océan-atmosphère. Ces études ont un double objectif, mieux comprendre le laboratoire numérique pour guider les voies de développement futures et ainsi concourir à son amélioration, mais aussi faire avancer nos connaissances sur les processus couplés qui régissent le système climatique. Dans ce mémoire, je retrace une partie de mes travaux en suivant ces deux axes : je présente dans un premier chapitre certains de mes travaux qui ont contribué à l’amélioration du modèle et dans un deuxième chapitre, des études plus spécifiques qui ont permis de faire avancer notre compréhension des processus couplés qui régissent le système climatique. Dans cette deuxième partie, je mets plus particulièrement en avant les travaux réalisés via l’encadrement d’étudiants (stages, thèses et post-doctorats).La communauté des modélisateurs du climat fait le constat qu’il est de plus en plus difficile d’accroître les performances des modèles, d’une part parce que l’accroissement de la complexité ne les rend pas forcément plus réalistes, mais aussi parce que notre marge de progression semble plus ténue. D’ailleurs, que veut dire améliorer leurs performances ? Cherche-t-on à développer un modèle universel qui permette d’étudier tous les processus climatiques ou bien devons-nous développer autant de modèles que d’applications ? Pour progresser, notre communauté se retrouve face à de nouveaux défis qu’elle tente de relever. Pour ma part, je propose de revenir sur la méthode de calibration des modèles. En effet, la calibration a été, jusque là, réalisée de façon heuristique et est peu documentée. Améliorer cette étape en développant une méthode objective permettra de renforcer notre confiance dans les modèles, de mieux qualifier leurs incertitudes paramétriques mais également d’affiner notre mise en œuvre de modèles à plus haute résolution