scholarly journals Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
David C. Lafferty ◽  
Ryan L. Sriver ◽  
Iman Haqiqi ◽  
Thomas W. Hertel ◽  
Klaus Keller ◽  
...  
Keyword(s):  
Climate Models ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Climate Information ◽  
Model Intercomparison ◽  
Temperature And Precipitation ◽  
Panel Model ◽  
Trade Offs ◽  
Intercomparison Project ◽  
Maize Yields

AbstractEfforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Here, we use a multi-model ensemble of statistically bias-corrected and downscaled climate models, as well as the corresponding parent models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), to drive a statistical panel model of U.S. maize yields that incorporates season-wide measures of temperature and precipitation. We analyze uncertainty in annual yield hindcasts, finding that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest weather-induced yield declines. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence.

scholarly journals Effective radiative forcing and adjustments in CMIP6 models

2020 ◽  
Vol 20 (16) ◽  
pp. 9591-9618 ◽  
Author(s):  
Christopher J. Smith ◽  
Ryan J. Kramer ◽  
Gunnar Myhre ◽  
Kari Alterskjær ◽  
William Collins ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Models ◽  
Coupled Model ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Aerosol Forcing ◽  
Intercomparison Project ◽  
Effective Radiative Forcing

Abstract. The effective radiative forcing, which includes the instantaneous forcing plus adjustments from the atmosphere and surface, has emerged as the key metric of evaluating human and natural influence on the climate. We evaluate effective radiative forcing and adjustments in 17 contemporary climate models that are participating in the Coupled Model Intercomparison Project (CMIP6) and have contributed to the Radiative Forcing Model Intercomparison Project (RFMIP). Present-day (2014) global-mean anthropogenic forcing relative to pre-industrial (1850) levels from climate models stands at 2.00 (±0.23) W m−2, comprised of 1.81 (±0.09) W m−2 from CO2, 1.08 (± 0.21) W m−2 from other well-mixed greenhouse gases, −1.01 (± 0.23) W m−2 from aerosols and −0.09 (±0.13) W m−2 from land use change. Quoted uncertainties are 1 standard deviation across model best estimates, and 90 % confidence in the reported forcings, due to internal variability, is typically within 0.1 W m−2. The majority of the remaining 0.21 W m−2 is likely to be from ozone. In most cases, the largest contributors to the spread in effective radiative forcing (ERF) is from the instantaneous radiative forcing (IRF) and from cloud responses, particularly aerosol–cloud interactions to aerosol forcing. As determined in previous studies, cancellation of tropospheric and surface adjustments means that the stratospherically adjusted radiative forcing is approximately equal to ERF for greenhouse gas forcing but not for aerosols, and consequentially, not for the anthropogenic total. The spread of aerosol forcing ranges from −0.63 to −1.37 W m−2, exhibiting a less negative mean and narrower range compared to 10 CMIP5 models. The spread in 4×CO2 forcing has also narrowed in CMIP6 compared to 13 CMIP5 models. Aerosol forcing is uncorrelated with climate sensitivity. Therefore, there is no evidence to suggest that the increasing spread in climate sensitivity in CMIP6 models, particularly related to high-sensitivity models, is a consequence of a stronger negative present-day aerosol forcing and little evidence that modelling groups are systematically tuning climate sensitivity or aerosol forcing to recreate observed historical warming.

Simulation of wind damages associated with the PRIMAVERA European windstorm event set

2021 ◽  
Author(s):  
Tristan Perotin
Keyword(s):  
Climate Models ◽  
Dynamical Downscaling ◽  
Insurance Industry ◽  
Coupled Model ◽  
Correction Method ◽  
Weather Data ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Insurance Sector ◽  
Intercomparison Project

<p>Winter windstorms are one of the major natural hazards affecting Europe, potentially causing large damages. The study of windstorm risks is therefore particularly important for the insurance industry. Physical natural catastrophe models for the insurance industry appeared in the 1980s and enable a fine analysis of the risk by taking into account all of its components (hazard, vulnerability and exposure). One main aspect of this catastrophe modeling is the production and validation of extreme hazard scenarios. As observational weather data is very sparse before the 1980s, estimates of extreme windstorm risks are usually based on climate models, despite the limited resolution of these models. Even though this limitation can be partially corrected by statistical or dynamical downscaling and calibration techniques, new generations of climate models can bring new understanding of windstorm risks.</p><p>In that context, PRIMAVERA, a European Union Horizon2020 project, made available a windstorm event set based on 21 tier 1 (1950-2014) highresSST-present simulations of the High Resolution Model Intercomparison Project (HighResMIP) component of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The events were identified with a storm tracking algorithm, footprints were defined for each event as maximum gusts over a 72 hour period, and the footprints were re-gridded to the ERA5 grid and calibrated with a quantile mapping correction method. The native resolution of these simulations ranges from 150km (typical resolution of the CMIP5 models) to 25km.</p><p>We have studied the applicability of the PRIMAVERA European windstorm event set for the modeling of European windstorm risks for the insurance sector. Preliminary results show that losses simulated from the event set appear to be consistent with historical data for all of the included simulations. The event set enables a better representation of attritional events and storm clustering than other existing event sets. An alternative calibration technique for extreme gusts and potential future developments of the event set will be proposed.</p>

scholarly journals Past warming trend constrains future warming in CMIP6 models

2020 ◽  
Author(s):  
Martin Stolpe ◽  
Katarzyna Tokarska ◽  
Sebastian Sippel ◽  
Erich Fischer ◽  
Christopher Smith ◽  
...  
Keyword(s):  
Climate Models ◽  
Coupled Model ◽  
Warming Trend ◽  
Paris Agreement ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Mitigation Scenario ◽  
Future Global Warming ◽  
Intercomparison Project ◽  
Future Warming

<div>Future global warming estimates have been similar across past assessments, but several climate models of the latest Sixth Coupled Model Intercomparison Project (CMIP6) simulate much stronger warming, apparently inconsistent with past assessments. Here we show that projected future warming is correlated with the simulated warming trend during recent decades across CMIP5 and CMIP6 models, enabling us to constrain future warming based on consistency with the observed warming. These findings carry important policy-relevant implications: the observationally-constrained CMIP6 median warming in high emissions and ambitious mitigation scenarios is over 16% and 14% lower by 2050 compared to the raw CMIP6 median, respectively, and over 14% and 8% lower by 2090, relative to 1995-2014. Observationally-constrained CMIP6 warming is consistent with previous assessments based on CMIP5 models, and in an ambitious mitigation scenario, the likely range is consistent with reaching the Paris Agreement target.</div><div> </div><div>Reference: </div><div>Tokarska, K.B.<sup>†</sup>, Stolpe, M.B.<sup>†</sup>, Sippel, S., Fischer, E.M., Smith, C.J., Lehner, F., and Knutti, R. (2020). Past warming trend constrains future warming in CMIP6 models. <em>Science Advances</em>  (accepted).</div><div><sup>†</sup>equal first authors</div>

scholarly journals Indirect Aerosol Effect Increases CMIP5 Models’ Projected Arctic Warming

2016 ◽  
Vol 29 (4) ◽  
pp. 1417-1428 ◽  
Author(s):  
Petr Chylek ◽  
Timothy J. Vogelsang ◽  
James D. Klett ◽  
Nicholas Hengartner ◽  
Dave Higdon ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Arctic Warming ◽  
Aerosol Effect ◽  
Cmip5 Climate Models ◽  
Intercomparison Project ◽  
Aerosol Effects

Abstract Phase 5 of the Coupled Model Intercomparison Project (CMIP5) climate models’ projections of the 2014–2100 Arctic warming under radiative forcing from representative concentration pathway 4.5 (RCP4.5) vary from 0.9° to 6.7°C. Climate models with or without a full indirect aerosol effect are both equally successful in reproducing the observed (1900–2014) Arctic warming and its trends. However, the 2014–2100 Arctic warming and the warming trends projected by models that include a full indirect aerosol effect (denoted here as AA models) are significantly higher (mean projected Arctic warming is about 1.5°C higher) than those projected by models without a full indirect aerosol effect (denoted here as NAA models). The suggestion is that, within models including full indirect aerosol effects, those projecting stronger future changes are not necessarily distinguishable historically because any stronger past warming may have been partially offset by stronger historical aerosol cooling. The CMIP5 models that include a full indirect aerosol effect follow an inverse radiative forcing to equilibrium climate sensitivity relationship, while models without it do not.

Contrasting contributions to future sea level under CMIP5 and CMIP6 scenarios from the Greenland and Antarctic ice sheets

2020 ◽  
Author(s):  
Tony Payne ◽  
Sophie Nowicki ◽  
Heiko Goelzer ◽  
Keyword(s):  
Sea Level ◽  
Climate Models ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Coupled Model ◽  
Greenland Ice Sheet ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Intercomparison Project ◽  
Antarctic Ice Sheets

<p>Projections of sea level contribution from the Greenland and Antarctic ice sheets rely on atmospheric and oceanic drivers obtained from climate models.  The Earth System Models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) generally project greater future warming compared to the previous CMIP5 effort. Here we use four CMIP6 models and a selection of CMIP5 models under two future climate scenarios to force multiple ice sheet models as part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). We find that the projected sea level contribution at 2100 from the multi ice sheet models under the CMIP6 scenarios falls within the CMIP5 range for the Antarctic ice sheet but is significantly increased for the Greenland ice sheet.  </p>

scholarly journals Past warming trend constrains future warming in CMIP6 models

2020 ◽  
Vol 6 (12) ◽  
pp. eaaz9549 ◽  
Author(s):  
Katarzyna B. Tokarska ◽  
Martin B. Stolpe ◽  
Sebastian Sippel ◽  
Erich M. Fischer ◽  
Christopher J. Smith ◽  
...  
Keyword(s):  
Climate Models ◽  
Coupled Model ◽  
Warming Trend ◽  
Paris Agreement ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Mitigation Scenario ◽  
Future Global Warming ◽  
Intercomparison Project ◽  
Future Warming

Future global warming estimates have been similar across past assessments, but several climate models of the latest Sixth Coupled Model Intercomparison Project (CMIP6) simulate much stronger warming, apparently inconsistent with past assessments. Here, we show that projected future warming is correlated with the simulated warming trend during recent decades across CMIP5 and CMIP6 models, enabling us to constrain future warming based on consistency with the observed warming. These findings carry important policy-relevant implications: The observationally constrained CMIP6 median warming in high emissions and ambitious mitigation scenarios is over 16 and 14% lower by 2050 compared to the raw CMIP6 median, respectively, and over 14 and 8% lower by 2090, relative to 1995–2014. Observationally constrained CMIP6 warming is consistent with previous assessments based on CMIP5 models, and in an ambitious mitigation scenario, the likely range is consistent with reaching the Paris Agreement target.

scholarly journals Experimental protocol for sealevel projections from ISMIP6 standalone ice sheet models

2020 ◽  
Author(s):  
Sophie Nowicki ◽  
Antony J. Payne ◽  
Heiko Goelzer ◽  
Helene Seroussi ◽  
William H. Lipscomb ◽  
...  
Keyword(s):  
Sea Level ◽  
Climate Models ◽  
Ice Sheet ◽  
Coupled Model ◽  
Sea Level Change ◽  
Model Intercomparison ◽  
Level Change ◽  
Ice Sheet Modeling ◽  
Emissions Scenarios ◽  
Intercomparison Project

Abstract. Projection of the contribution of ice sheets to sea-level change as part of the Coupled Model Intercomparison Project – phase 6 (CMIP6) takes the form of simulations from coupled ice-sheet-climate models and standalone ice sheet models, overseen by the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). This paper describes the experimental setup for process-based sea-level change projections to be performed with standalone Greenland and Antarctic ice sheet models in the context of ISMIP6. The ISMIP6 protocol relies on a suite of polar atmospheric and oceanic CMIP-based forcing for ice sheet models, in order to explore the uncertainty in projected sea-level change due to future emissions scenarios, CMIP models, ice sheet models, and parameterizations for ice-ocean interactions. We describe here the approach taken for defining the suite of ISMIP6 standalone ice sheet simulations, document the experimental framework and implementation, as well as present an overview of the ISMIP6 forcing to be used by participating ice sheet modeling groups.

scholarly journals Diagnosis of Multiyear Predictability on Continental Scales

2011 ◽  
Vol 24 (19) ◽  
pp. 5108-5124 ◽  
Author(s):  
Liwei Jia ◽  
Timothy DelSole
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Climate Models ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Optimization Method ◽  
First Year ◽  
Temperature And Precipitation ◽  
Regional Prediction ◽  
Intercomparison Project

A new statistical optimization method is used to identify components of surface air temperature and precipitation on six continents that are predictable in multiple climate models on multiyear time scales. The components are identified from unforced “control runs” of the Coupled Model Intercomparison Project phase 3 dataset. The leading predictable components can be calculated in independent control runs with statistically significant skill for 3–6 yr for surface air temperature and 1–3 yr for precipitation, depending on the continent, using a linear regression model with global sea surface temperature (SST) as a predictor. Typically, lag-correlation maps reveal that the leading predictable components of surface air temperature are related to two types of SST patterns: persistent patterns near the continent itself and an oscillatory ENSO-like pattern. The only exception is Europe, which has no significant ENSO relation. The leading predictable components of precipitation are significantly correlated with an ENSO-like SST pattern. No multiyear predictability of land precipitation could be verified in Europe. The squared multiple correlations of surface air temperature and precipitation for nonzero lags on each continent are less than 0.4 in the first year, implying that less than 40% of variations of the leading predictable component can be predicted from global SST. The predictable components describe the spatial structures that can be predicted on multiyear time scales in the absence of anthropogenic and natural forcing, and thus provide a scientific rationale for regional prediction on multiyear time scales.

scholarly journals AerChemMIP: quantifying the effects of chemistry and aerosols in CMIP6

2017 ◽  
Vol 10 (2) ◽  
pp. 585-607 ◽  
Author(s):  
William J. Collins ◽  
Jean-François Lamarque ◽  
Michael Schulz ◽  
Olivier Boucher ◽  
Veronika Eyring ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Chemistry ◽  
Radiative Forcing ◽  
Climate Models ◽  
Coupled Model ◽  
Climate Impacts ◽  
Atmospheric Composition ◽  
Historical Period ◽  
Model Intercomparison ◽  
Intercomparison Project

Abstract. The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) is endorsed by the Coupled-Model Intercomparison Project 6 (CMIP6) and is designed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. These are specifically near-term climate forcers (NTCFs: methane, tropospheric ozone and aerosols, and their precursors), nitrous oxide and ozone-depleting halocarbons. The aim of AerChemMIP is to answer four scientific questions. 1. How have anthropogenic emissions contributed to global radiative forcing and affected regional climate over the historical period? 2. How might future policies (on climate, air quality and land use) affect the abundances of NTCFs and their climate impacts? 3.How do uncertainties in historical NTCF emissions affect radiative forcing estimates? 4. How important are climate feedbacks to natural NTCF emissions, atmospheric composition, and radiative effects? These questions will be addressed through targeted simulations with CMIP6 climate models that include an interactive representation of tropospheric aerosols and atmospheric chemistry. These simulations build on the CMIP6 Diagnostic, Evaluation and Characterization of Klima (DECK) experiments, the CMIP6 historical simulations, and future projections performed elsewhere in CMIP6, allowing the contributions from aerosols and/or chemistry to be quantified. Specific diagnostics are requested as part of the CMIP6 data request to highlight the chemical composition of the atmosphere, to evaluate the performance of the models, and to understand differences in behaviour between them.

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