Julien Boé

Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty

Model-based projections of shifts in tree species range due to climate change are becoming an important decision support tool for forest management. However, poorly evaluated sources of uncertainty require more scrutiny before relying heavily on models for decision-making. We evaluated uncertainty arising from differences in model formulations of tree response to climate change based on a rigorous intercomparison of projections of tree distributions in France. We compared eight models ranging from niche-based to process-based models. On average, models project large range contractions of temperate tree species in lowlands due to climate change. There was substantial disagreement between models for temperate broadleaf deciduous tree species, but differences in the capacity of models to account for rising CO(2) impacts explained much of the disagreement. There was good quantitative agreement among models concerning the range contractions for Scots pine. For the dominant Mediterranean tree species, Holm oak, all models foresee substantial range expansion.

Land–sea contrast, soil-atmosphere and cloud-temperature interactions: interplays and roles in future summer European climate change

Europe and in particular its southern part are expected to undergo serious climate changes during summer in response to anthropogenic forcing, with large surface warming and decrease in precipitation. Yet, serious uncertainties remain, especially over central and western Europe. Several mechanisms have been suggested to be important in that context but their relative importance and possible interplays are still not well understood. In this paper, the role of soil-atmosphere interactions, cloud-temperature interactions and land–sea warming contrast in summer European climate change and how they interact are analyzed. Models for which evapotranspiration is strongly limited by soil moisture in the present climate are found to tend to simulate larger future decrease in evapotranspiration. Models characterized by stronger present-day anti-correlation between cloud cover and temperature over land tend to simulate larger future decrease in cloud cover. Large model-to-model differences regarding land–sea warming contrast and its impacts are also found. Warming over land is expected to be larger than warming over sea, leading to a decrease in continental relative humidity and precipitation because of the discrepancy between the change in atmospheric moisture capacity over land and the change in specific humidity. Yet, it is not true for all the models over our domain of interest. Models in which evapotranspiration is not limited by soil moisture and with a weak present-day anti-correlation between cloud cover and temperature tend to simulate smaller land surface warming. In these models, change in specific humidity over land is therefore able to match the continental increase in moisture capacity, which leads to virtually no change in continental relative humidity and smaller precipitation change. Because of the physical links that exist between the response to anthropogenic forcing of important impact-related climate variables and the way some mechanisms are simulated in the context of present-day variability, this study suggests some potentially useful metrics to reduce summer European climate change uncertainties.

Transferability in the future climate of a statistical downscaling method for precipitation in France

A statistical downscaling approach for precipitation in France based on the analog method and its evaluation for different combinations of predictors is described, with focus on the transferability of the method to the future climate. First, the realism of downscaled present-day precipitation climatology and interannual variability for different combinations of predictors from four reanalyses is assessed. Satisfactory results are obtained, but elaborated predictors do not lead to major and consistent across-reanalyses improvements. The downscaling method is then evaluated on its capacity to capture precipitation trends in the last decades. As uncertainties in downscaled trends due to the choice of the reanalysis are large and observed trends are weak, this analysis does not lead to strong conclusions on the applicability of the method to a changing climate. The temporal transferability is then assessed thanks to a perfect model framework. The statistical downscaling relationship is built using present-day predictors and precipitation simulated by 12 regional climate models. The entire projections are then downscaled, and future downscaled and simulated precipitation changes are compared. A good temporal transferability is obtained only with a specific combination of predictors. Finally, the regional climate models are downscaled, thanks to the relationship built with reanalyses and observations, for the best combination of predictors. Results are similar to the changes simulated by the models, which reinforces our confidence in the realism of the models and of the downscaling method. Uncertainties in precipitation change due to reanalyses are found to be limited compared to those due to regional simulations.

Influence of small-scale North Atlantic sea surface temperature patterns on the marine boundary layer and free troposphere: a study using the atmospheric ARPEGE model

A high-resolution global atmospheric model is used to investigate the influence of the representation of small-scale North Atlantic sea surface temperature (SST) patterns on the atmosphere during boreal winter. Two ensembles of forced simulations are performed and compared. In the first ensemble (HRES), the full spatial resolution of the SST is maintained while small-scale features are smoothed out in the Gulf Stream region for the second ensemble (SMTH). The model shows a reasonable climatology in term of large-scale circulation and air–sea interaction coefficient when compared to reanalyses and satellite observations, respectively. The impact of small-scale SST patterns as depicted by differences between HRES and SMTH shows a strong meso-scale local mean response in terms of surface heat fluxes, convective precipitation, and to a lesser extent cloudiness. The main mechanism behind these statistical differences is that of a simple hydrostatic pressure adjustment related to increased SST and marine atmospheric boundary layer temperature gradient along the North Atlantic SST front. The model response to small-scale SST patterns also includes remote large-scale effects: upper tropospheric winds show a decrease downstream of the eddy-driven jet maxima over the central North Atlantic, while the subtropical jet exhibits a significant northward shift in particular over the eastern Mediterranean region. Significant changes are simulated in regard to the North Atlantic storm track, such as a southward shift of the storm density off the coast of North America towards the maximum SST gradient. A storm density decrease is also depicted over Greenland and the Nordic seas while a significant increase is seen over the northern part of the Mediterranean basin. Changes in Rossby wave breaking frequencies and weather regimes spatial patterns are shown to be associated to the jets and storm track changes.

Modulation of soil moisture–precipitation interactions over France by large scale circulation

How soil moisture affects precipitation is an important question—with far reaching consequences, from weather prediction to centennial climate change—, albeit a poorly understood one. In this paper, an analysis of soil moisture–precipitation interactions over France based on observations is presented. A first objective of this paper is to investigate how large scale circulation modulates soil moisture–precipitation interactions, thanks to a weather regime approach. A second objective is to study the influence of soil moisture not only on precipitation but also on the difference between precipitation and evapotranspiration. Indeed, to have a total positive soil moisture–precipitation feedback, the potential decrease in precipitation associated with drier soils should be larger than the decrease in evapotranspiration that drier soils may also cause. A potential limited impact of soil moisture on precipitation is found for some weather regimes, but its sign depends on large scale circulation. Indeed, antecedent dry soil conditions tend to lead to smaller precipitation for the negative phase of the North Atlantic Oscillation (NAO) regime but to larger precipitation for the Atlantic Low regime. This differential response of precipitation to soil moisture anomalies depending on large scale circulation is traced back to different responses of atmospheric stability. For all circulation regimes, dry soils tend to increase the lifted condensation level, which is unfavorable to precipitation. But for the negative phase of the NAO, low soil moisture tends to lead to an increase of atmospheric stability while it tends to lead to a decrease of stability for Atlantic Low. Even if the impact of soil moisture anomalies varies depending on large scale circulation (it is larger for Atlantic low and the positive phase of the NAO), dry soils always lead to a decrease in evapotranspiration. As the absolute effect of antecedent soil moisture on evapotranspiration is always much larger than its effects on precipitation, for all circulation regimes dry soil anomalies subsequently lead to positive precipitation minus evapotranspiration anomalies i.e. the total soil moisture feedback is found to be negative. This negative feedback is stronger for the Atlantic Low and the positive phase of the NAO regimes.

Projected 21st century snowfall changes over the French Alps and related uncertainties

Snowfall changes in mountain areas in response to anthropogenic forcing could have widespread hydrological, ecological and economic impacts. In this paper, the robustness of snowfall changes over the French Alps projected during the 21st century and the associated uncertainties are studied. In particular, the role of temperature changes on snowfall changes is investigated. Those issues are tackled through the analysis of the results of a very large ensemble of high-resolution regional climate projections, obtained either through dynamical or statistical downscaling. We find that, at the beginning and at the end of the cold season extending from November to March (included), temperature change is an important source of spread in snowfall changes. However, no link is found between temperature and snowfall changes in January and February. At the beginning and at the end of the cold season, the rate of change in snowfall per Kelvin does not depend much on the bias correction step, the period or the greenhouse gas scenario but mostly on the downscaling method and the climate models, the latter uncertainty source being dominant.

Emergence of human influence on summer record‐breaking temperatures over Europe

Observational analysis of Europe summer record-breaking temperatures suggests that their occurrence differs from that expected in a stationary climate since the late 1980s. The observed cold and warm record evolution is well simulated by the ensemble mean of 27 coupled models from the Coupled Model Intercomparison Project phase 5 (CMIP5). We find that this evolution is still today within the range of internal variability derived from CMIP5 preindustrial simulations. We then estimate a time of emergence of the summer record anthropogenic influence in a world under a business as usual greenhouse gas emission scenario. We suggest a time of emergence around 2020 for the cold records and 2030 for the warm ones with an uncertainty of ± 20 years. By 2100, the multimodel ensemble mean indicates a tenfold increase of the number of warm records compared to the first half of the twentieth century and the quasi-disappearance of cold records.

Detection of anthropogenic influence on the evolution of record-breaking temperatures over Europe

Changes in temperature extreme events are expected as a result of anthropogenic climate change, but uncertainties exist in when and how these changes will be manifest regionally. This is especially the case over Europe due to different methodologies and definitions of temperature extreme events. An alternative approach is to examine changes in record-breaking temperatures. Datasets of observed temperature combined with ensembles of climate model simulations are used to assess the possible causes and significance of record-breaking temperature changes over the late twentieth and twenty-first centuries. A simple detection methodology is first applied to evaluate the extent to which the effect of anthropogenic forcing can be detected in present-day observed and simulated changes in record-breaking temperature. We then study the projected evolution of record-breaking daily minimum and maximum temperatures over the twenty-first century in Europe with a climate model. The same detection approach is used to identify the time of emergence of the anthropogenic signal relative to a model-derived estimate of internal variability. From the 1980s onwards, a change in the evolution of cold and warm records is observed and simulated, but it still remains in the range of internal variability until the end of the twentieth century. Minimum and maximum record-breaking temperatures tend to occur (respectively) less and more often than during the 1960s and 1970s taken as representative of a stationary climate. Model simulations with natural forcing only fail to reproduce the observed changes after the 1980s while the latter are compatible with simulations constrained by anthropogenic forcings. The deviation from the characteristic behavior of a stationary climate record-wise initiated in the 1980s is projected to accentuate during the twenty-first century. Annual changes become inconsistent with the model-derived internal variability between the 2020s and 2030s. Over the last three decades of the twenty-first century and under the RCP8.5 scenario, warm records occur on average five times more often than initially. Conversely, breaking new cold record become extremely difficult. The Mediterranean region is particularly affected in summer, whereas central and northeastern Europe is more impacted in winter.

Modulation of the summer hydrological cycle evolution over western Europe by anthropogenic aerosols and soil‐atmosphere interactions

Large decadal variations in solar radiation at surface have been observed over Europe for sixty years. These variations might have impacted the hydrological cycle, through a modulation of the energy available for evapotranspiration. Here, a large ensemble of climate models is analyzed to characterize the impacts of anthropogenic aerosols on the hydrological cycle over western Europe in summer, and the associated uncertainties. Some models simulate strong aerosols-driven changes in evapotranspiration and also precipitation on the historical period while other models show virtually no impact. These opposed responses are largely determined by two seemingly independent properties of the models: the magnitude of the impact of anthropogenic aerosols on solar radiation and whether evapotranspiration is predominantly water or energy limited. Both properties, characterized on the past climate, are highly uncertain in current climate models, and continue to impact the evolution of the hydrological cycle through the 21st century.

Can metric-based approaches really improve multi-model climate projections? The case of summer temperature change in France

AbstractThe multi-model ensemble mean is generally used as a default approach to estimate climate change signals, based on the implicit hypothesis that all models provide equally credible projections. As this hypothesis is unlikely to be true, it is in theory possible to obtain more realistic projections by giving more weight to more realistic models according to a relevant metric, if such a metric exists. This alternative approach however raises many methodological issues. In this study, a methodological framework based on a perfect model approach is described. It is intended to provide some useful elements of answer to these methodological issues. The basic idea is to take a random climate model and treat it as if it were the truth (or “synthetic observations”). Then, all the other members from the multi-model ensemble are used to derive thanks to a metric-based approach a posterior estimate of the future change, based on the synthetic observation of the metric. This posterior estimate can be compared to the synthetic observation of future change to evaluate the skill of the approach. This general framework is applied to future summer temperature change in France. A process-based metric, related to cloud-temperature interactions is tested, with different simple statistical methods to combine multiple model results (e.g. weighted average, model selection, regression.) Except in presence of large observational errors in the metric, metric-based methods using the metric related to cloud temperature interactions generally lead to large reductions of errors compared to the ensemble mean, but the sensitivity to methodological choices is important .