Basin-Informed Flood Frequency Curves
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Pallotta Goldhahn, Giuliana↗
Engineering topics
Bonfils, Celine
Publications and source records attributed to Bonfils, Celine.
Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3
Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction.
54 ENVIRONMENTAL SCIENCES↗
External Influences on Modeled and Observed Cloud Trends
Understanding the cloud response to external forcing is a major challenge for climate science. This crucial goal is complicated by intermodel differences in simulating present and future cloud cover and by observational uncertainty. This is the first formal detection and attribution study of cloud changes over the satellite era. Presented herein are CMIP5 (Coupled Model Intercomparison Project - Phase 5) model-derived fingerprints of externally forced changes to three cloud properties: the latitudes at which the zonally averaged total cloud fraction (CLT) is maximized or minimized, the zonal average CLT at these latitudes, and the height of high clouds at these latitudes. By considering simultaneous changes in all three properties, the authors define a coherent multivariate fingerprint of cloud response to external forcing and use models from phase 5 of CMIP (CMIP5) to calculate the average time to detect these changes. It is found that given perfect satellite cloud observations beginning in 1983, the models indicate that a detectable multivariate signal should have already emerged. A search is then made for signals of external forcing in two observational datasets: ISCCP (International Satellite Cloud Climatology Project) and PATMOS-x (Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres - Extended). The datasets are both found to show a poleward migration of the zonal CLT pattern that is incompatible with forced CMIP5 models. Nevertheless, a detectable multivariate signal is predicted by models over the PATMOS-x time period and is indeed present in the dataset. Despite persistent observational uncertainties, these results present a strong case for continued efforts to improve these existing satellite observations, in addition to planning for new missions.
clouds (meteorology)↗
Volcanic Contribution to Decadal Changes in Tropospheric Temperature
Despite continued growth in atmospheric levels of greenhouse gases, global mean surface and tropospheric temperatures have shown slower warming since 1998 than previously. Possible explanations for the slow-down include internal climate variability, external cooling influences and observational errors. Several recent modelling studies have examined the contribution of early twenty-first-century volcanic eruptions to the muted surface warming. Here we present a detailed analysis of the impact of recent volcanic forcing on tropospheric temperature, based on observations as well as climate model simulations. We identify statistically significant correlations between observations of stratospheric aerosol optical depth and satellite-based estimates of both tropospheric temperature and short-wave fluxes at the top of the atmosphere. We show that climate model simulations without the effects of early twenty-first-century volcanic eruptions overestimate the tropospheric warming observed since 1998. In two simulations with more realistic volcanic influences following the 1991 Pinatubo eruption, differences between simulated and observed tropospheric temperature trends over the period 1998 to 2012 are up to 15% smaller, with large uncertainties in the magnitude of the effect. To reduce these uncertainties, better observations of eruption-specific properties of volcanic aerosols are needed, as well as improved representation of these eruption-specific properties in climate model simulations.
Atmospheric temperature↗