Increased Amazon basin wet-season precipitation and river discharge since the early 1990s driven by tropical Pacific variability

International audienceThe Amazon Basin, the largest watershed on Earth, experienced a significant increase in wet-season precipitation and high-season river discharge from the early 1990s to early 2010s. Some studies have linked the increased Amazon Basin hydrologic cycle to decadal trends of increased Pacific trade winds, eastern Pacific sea surface temperature (SST) cooling, and associated strengthening of the Pacific Walker circulation. However, it has been difficult to disentangle the role of Pacific decadal variability from the impacts of greenhouse gases and other external climate drivers over the same period. Here, we separate the contributions of external forcings from those of Pacific decadal variability by comparing two large ensembles of climate model experiments with identical radiative forcing agents but imposing different tropical Pacific wind stress. One ensemble constrains tropical Pacific wind stress to its long-term climatology, suppressing tropical Pacific decadal variability; the other ensemble imposes the observed tropical Pacific wind stress anomalies, simulating realistic tropical Pacific decadal variability. Comparing the Amazon Basin hydroclimate response in the two ensembles allows us to distinguish the contributions of external forcings common to both simulations from those related to Pacific trade wind variability. For the 1992–2012 trend, the experiments with observed tropical Pacific wind stress anomalies simulate strengthening of the Walker circulation between the Pacific and South America and sharpening of the Pacific–Atlantic interbasin SST contrast, driving increased Amazon Basin wet-season precipitation and high-season discharge. In contrast, these circulation and hydrologic intensification trends are absent in the simulations with climatological tropical Pacific wind stress. This work underscores the importance of Pacific decadal variability in driving hydrological cycle changes and modulating the hydroclimate impacts of global warming over the Amazon Basin

Coupling statistically downscaled GCM outputs with a basin-lake hydrological model in subtropical South America: evaluation of the influence of large-scale precipitation changes on regional hydroclimate variability

International audienceWe explore the reliability of large-scale climate variables, namely precipitation and temperature, as inputs for a basin-lake hydrological model in central Argentina. We used data from two regions in NCEP/NCAR reanalyses and three regions from LMDZ model simulations forced with observed sea surface temperature (HadISST) for the last 50 years. Reanalyses data cover part of the geographical area of the Sali-Dulce Basin (region A) and a zone at lower latitudes (region B). The LMDZ selected regions represent the geographical area of the Sali-Dulce Basin (box A), and two areas outside of the basin at lower latitudes (boxes B and C). A statistical downscaling method is used to connect the large-scale climate variables inferred from LMDZ and the reanalyses, with the hydrological Soil Water Assessment Tool (SWAT) model in order to simulate the Rio Sali-Dulce discharge during 1950-2005. The SWAT simulations are then used to force the water balance of Laguna Mar Chiquita, which experienced an abrupt level rise in the 1970's attributed to the increase in Rio Sali-Dulce discharge. Despite that the lowstand in the 1970's is not well reproduced in either simulation, the key hydrological cycles in the lake level are accurately captured. Even though satisfying results are obtained with the SWAT simulations using downscaled reanalyses, the lake level are more realistically simulated with the SWAT simulations using downscaled LMDZ data in boxes B and C, showing a strong climate influence from the tropics on lake level fluctuations. Our results highlight the ability of downscaled climatic data to reproduce regional climate features. Laguna Mar Chiquita can therefore be considered as an integrator of large-scale climate changes since the forcing scenarios giving best results are those relying on global climate simulations forced with observed sea surface temperature. This climate-basin-lake model is a promising approach for understanding and simulating long-term lake level variations

Toward predicting volcanically-forced decadal climate variability

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Progress in paleoclimate modeling

International audienceThis paper briefly surveys areas of paleoclimate modeling notable for recent progress. New ideas, including hypotheses giving a pivotal role to sea ice, have revitalized the low-order models used to simulate the time evolution of glacial cycles through the Pleistocene, a prohibitive length of time for comprehensive general circulation models (GCMs). In a recent breakthrough, however, GCMs have succeeded in simulating the onset of glaciations. This occurs at times (most recently, 115 kyr B.P.) when high northern latitudes are cold enough to maintain a snow cover and tropical latitudes are warm, enhancing the moisture source. More generally, the improvement in models has allowed simulations of key periods such as the Last Glacial Maximum and the mid-Holocene that compare more favorably and in more detail with paleoproxy data. These models now simulate ENSO cycles, and some of them have been shown to reproduce the reduction of ENSO activity observed in the early to middle Holocene. Modeling studies have demonstrated that the reduction is a response to the altered orbital configuration at that time. An urgent challenge for paleoclimate modeling is to explain and to simulate the abrupt changes observed during glacial epochs (i.e., Dansgaard-Oescher cycles, Heinrich events, and the Younger Dryas). Efforts have begun to simulate the last millennium. Over this time the forcing due to orbital variations is less important than the radiance changes due to volcanic eruptions and variations in solar output. Simulations of these natural variations test the models relied on for future climate change projections. They provide better estimates of the internal and naturally forced variability at centennial time scales, elucidating how unusual the recent global temperature trends are

Effects of forcing differences and initial conditions on inter-model agreement in the VolMIP volc-pinatubo-full experiment

International audienceThis paper provides initial results from a multi-model ensemble analysis based on the volc-pinatubo-full experiment performed within the Model Intercomparison Project on the climatic response to volcanic forcing (VolMIP) as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The volc-pinatubo-full experiment is based on ensemble of volcanic forcing-only climate simulations with the same volcanic aerosol dataset across the participating models (the 1991-1993 Pinatubo period from the CMIP6-GloSSAC dataset). The simulations are conducted within an idealized experimental design where initial states are sampled consistently across models from the CMIP6-piControl simulation providing unperturbed pre-industrial background conditions. The multi-model ensemble includes output from an initial set of six participating Earth system models (CanESM5, GISS-E2.1-G, IPSL-CM6A-LR, MIROC-E2SL, MPI-ESM1.2-LR and UKESM1).The results show overall good agreement between the different models on the global and hemispheric scale concerning the surface climate responses, thus demonstrating the overall effectiveness of VolMIP’s experimental design. However, small yet significant inter-model discrepancies are found in radiative fluxes especially in the tropics, that preliminary analyses link with minor differences in forcing implementation, model physics, notably aerosol-radiation interactions, the simulation and sampling of El Niño-Southern Oscillation (ENSO) and, possibly, the simulation of climate feedbacks operating in the tropics. We discuss the volc-pinatubo-full protocol and highlight the advantages of volcanic forcing experiments defined within a carefully designed protocol with respect to emerging modeling approaches based on large ensemble transient simulations. We identify how the VolMIP strategy could be improved in future phases of the initiative to ensure a cleaner sampling protocolwith greater focus on the evolving state of ENSO in the pre-eruption period

"Attribuer" les variations climatiques observées

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Large ensemble particle filter for spatial climate reconstructions using a Linear inverse model

International audienceProxy records that document the last 2000 years climate provide evidences for the wide range of the natural climate variability from inter-annual to secular timescales not captured by the short window of recent direct observations. Assessing climate models ability to reproduce such natural variations is crucial to understand climate sensitivity and impacts of future climate change. Paleoclimate data assimilation (PDA) offers a powerful way to extend the short instrumental period by optimally combining the physics described by General Circulation Climate Models (GCMs) with information from available proxy records while taking into account their uncertainties. Here we present a new PDA approach based on a sequential importance resampling (SIR) particle filter that uses Linear Inverse Modeling (LIM) as an emulator of several CMIP-class GCMs. We examine in a perfect-model framework the skill of the various LIMs to forecast the dynamic of the surface temperatures and provide spatial field reconstructions over the last millennium in a SIR particle filter. Our results show that the LIMs allow for skilful ensemble forecasts at one-year lead-time based on GCMs dynamical knowledge with best prediction in the tropics and the North Atlantic. The PDA further provides a set of physically consistent spatial fields allowing robust uncertainty quantification related to climate models biases and proxy spatial sampling. Our results indicate that the LIM yields dynamical memory improving climate variability reconstructions and support the use of the LIM as a GCM44emulator in real reconstruction to propagate large ensembles of particles at low cost in SIR particle filter

Large ensemble particle filter for spatial climate reconstructions using a Linear inverse model

International audienceProxy records that document the last 2000 years climate provide evidences for the wide range of the natural climate variability from inter-annual to secular timescales not captured by the short window of recent direct observations. Assessing climate models ability to reproduce such natural variations is crucial to understand climate sensitivity and impacts of future climate change. Paleoclimate data assimilation (PDA) offers a powerful way to extend the short instrumental period by optimally combining the physics described by General Circulation Climate Models (GCMs) with information from available proxy records while taking into account their uncertainties. Here we present a new PDA approach based on a sequential importance resampling (SIR) particle filter that uses Linear Inverse Modeling (LIM) as an emulator of several CMIP-class GCMs. We examine in a perfect-model framework the skill of the various LIMs to forecast the dynamic of the surface temperatures and provide spatial field reconstructions over the last millennium in a SIR particle filter. Our results show that the LIMs allow for skilful ensemble forecasts at one-year lead-time based on GCMs dynamical knowledge with best prediction in the tropics and the North Atlantic. The PDA further provides a set of physically consistent spatial fields allowing robust uncertainty quantification related to climate models biases and proxy spatial sampling. Our results indicate that the LIM yields dynamical memory improving climate variability reconstructions and support the use of the LIM as a GCM44emulator in real reconstruction to propagate large ensembles of particles at low cost in SIR particle filter

Large ensemble particle filter for spatial climate reconstructions using a Linear inverse model

International audienceProxy records that document the last 2000 years climate provide evidences for the wide range of the natural climate variability from inter-annual to secular timescales not captured by the short window of recent direct observations. Assessing climate models ability to reproduce such natural variations is crucial to understand climate sensitivity and impacts of future climate change. Paleoclimate data assimilation (PDA) offers a powerful way to extend the short instrumental period by optimally combining the physics described by General Circulation Climate Models (GCMs) with information from available proxy records while taking into account their uncertainties. Here we present a new PDA approach based on a sequential importance resampling (SIR) particle filter that uses Linear Inverse Modeling (LIM) as an emulator of several CMIP-class GCMs. We examine in a perfect-model framework the skill of the various LIMs to forecast the dynamic of the surface temperatures and provide spatial field reconstructions over the last millennium in a SIR particle filter. Our results show that the LIMs allow for skilful ensemble forecasts at one-year lead-time based on GCMs dynamical knowledge with best prediction in the tropics and the North Atlantic. The PDA further provides a set of physically consistent spatial fields allowing robust uncertainty quantification related to climate models biases and proxy spatial sampling. Our results indicate that the LIM yields dynamical memory improving climate variability reconstructions and support the use of the LIM as a GCM44emulator in real reconstruction to propagate large ensembles of particles at low cost in SIR particle filter