scholarly journals On the Correspondence between Mean Forecast Errors and Climate Errors in CMIP5 Models

2014 ◽  
Vol 27 (4) ◽  
pp. 1781-1798 ◽  
Author(s):  
H.-Y. Ma ◽  
S. Xie ◽  
S. A. Klein ◽  
K. D. Williams ◽  
J. S. Boyle ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Systematic Errors ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Short Term ◽  
Climate Simulations ◽  
Intercomparison Project ◽  
Short And Long Term

Abstract The present study examines the correspondence between short- and long-term systematic errors in five atmospheric models by comparing the 16 five-day hindcast ensembles from the Transpose Atmospheric Model Intercomparison Project II (Transpose-AMIP II) for July–August 2009 (short term) to the climate simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and AMIP for the June–August mean conditions of the years of 1979–2008 (long term). Because the short-term hindcasts were conducted with identical climate models used in the CMIP5/AMIP simulations, one can diagnose over what time scale systematic errors in these climate simulations develop, thus yielding insights into their origin through a seamless modeling approach. The analysis suggests that most systematic errors of precipitation, clouds, and radiation processes in the long-term climate runs are present by day 5 in ensemble average hindcasts in all models. Errors typically saturate after few days of hindcasts with amplitudes comparable to the climate errors, and the impacts of initial conditions on the simulated ensemble mean errors are relatively small. This robust bias correspondence suggests that these systematic errors across different models likely are initiated by model parameterizations since the atmospheric large-scale states remain close to observations in the first 2–3 days. However, biases associated with model physics can have impacts on the large-scale states by day 5, such as zonal winds, 2-m temperature, and sea level pressure, and the analysis further indicates a good correspondence between short- and long-term biases for these large-scale states. Therefore, improving individual model parameterizations in the hindcast mode could lead to the improvement of most climate models in simulating their climate mean state and potentially their future projections.

scholarly journals A return to large-scale features of Pliocene climate: the Pliocene Model Intercomparison Project Phase 2

2020 ◽  
Author(s):  
Alan M. Haywood ◽  
Julia C. Tindall ◽  
Harry J. Dowsett ◽  
Aisling M. Dolan ◽  
Kevin M. Foley ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Temperature Response ◽  
Surface Air Temperature ◽  
Co2 Concentration ◽  
Mean Value ◽  
System Response ◽  
Phase 2 ◽  
Model Intercomparison ◽  
Intercomparison Project

Abstract. The Pliocene epoch has great potential to improve our understanding of the long-term climatic and environmental consequences of an atmospheric CO2 concentration near ~ 400 parts per million by volume. Here we present the large-scale features of Pliocene climate as simulated by a new ensemble of climate models of varying complexity and spatial resolution and based on new reconstructions of boundary conditions (the Pliocene Model Intercomparison Project Phase 2; PlioMIP2). As a global annual average, modelled surface air temperatures increase by between 1.4 and 4.7 °C relative to pre-industrial with a multi-model mean value of 2.8 °C. Annual mean total precipitation rates increase by 6 % (range: 2 %–13 %). On average, surface air temperature (SAT) increases are 1.3 °C greater over the land than over the oceans, and there is a clear pattern of polar amplification with warming polewards of 60° N and 60° S exceeding the global mean warming by a factor of 2.4. In the Atlantic and Pacific Oceans, meridional temperature gradients are reduced, while tropical zonal gradients remain largely unchanged. Although there are some modelling constraints, there is a statistically significant relationship between a model's climate response associated with a doubling in CO2 (Equilibrium Climate Sensitivity; ECS) and its simulated Pliocene surface temperature response. The mean ensemble earth system response to doubling of CO2 (including ice sheet feedbacks) is approximately 50 % greater than ECS, consistent with results from the PlioMIP1 ensemble. Proxy-derived estimates of Pliocene sea-surface temperatures are used to assess model estimates of ECS and indicate a range in ECS from 2.5 to 4.3 °C. This result is in general accord with the range in ECS presented by previous IPCC Assessment Reports.

scholarly journals Modelling large timescale and small timescale service variability

2019 ◽  
Vol 293 (1) ◽  
pp. 123-140
Author(s):  
Marco Gribaudo ◽  
Illés Horváth ◽  
Daniele Manini ◽  
Miklós Telek
Keyword(s):  
Service Time ◽  
Large Scale ◽  
Service Time Distribution ◽  
Efficient Computation ◽  
Short Term ◽  
Service Unit ◽  
Long Term Effect ◽  
Short And Long Term ◽  
Short Term Variability

Abstract The performance of service units may depend on various randomly changing environmental effects. It is quite often the case that these effects vary on different timescales. In this paper, we consider small and large scale (short and long term) service variability, where the short term variability affects the instantaneous service speed of the service unit and a modulating background Markov chain characterizes the long term effect. The main modelling challenge in this work is that the considered small and long term variation results in randomness along different axes: short term variability along the time axis and long term variability along the work axis. We present a simulation approach and an explicit analytic formula for the service time distribution in the double transform domain that allows for the efficient computation of service time moments. Finally, we compare the simulation results with analytic ones.

scholarly journals Short- and Long-Term Effects of Nonconsciously Processed Ambient Scents in a Servicescape: Findings From Two Field Experiments

2019 ◽  
Vol 22 (4) ◽  
pp. 440-455 ◽  
Author(s):  
Anna Girard ◽  
Marcel Lichters ◽  
Marko Sarstedt ◽  
Dipayan Biswas
Keyword(s):  
Large Scale ◽  
Field Experiments ◽  
Field Studies ◽  
Long Term Effects ◽  
Short Term ◽  
Railway Company ◽  
Short And Long Term ◽  
Ambient Scents ◽  
Short Term Effects

Ambient scents are being increasingly used in different service environments. While there is emerging research on the effects of scents, almost nothing is known about the long-term effects of consumers’ repeated exposure to ambient scents in a service environment as prior studies on ambient scents have been lab or field studies examining short-term effects of scent exposure only. Addressing this limitation, we examine the short- and long-term effects of ambient scents. Specifically, we present a conceptual framework for the short- and long-term effects of nonconsciously processed ambient scent in olfactory-rich servicescapes. We empirically test this framework with the help of two large-scale field experiments, conducted in collaboration with a major German railway company, in which consumers were exposed to a pleasant, nonconsciously processed scent. The first experiment demonstrates ambient scent’s positive short-term effects on consumers’ service perceptions. The second experiment—a longitudinal study conducted over a 4-month period—examines scent’s long-term effects on consumers’ reactions and demonstrates that the effects persist even when the scent has been removed from the servicescape.

The Double-ITCZ Bias in CMIP6 Models

2020 ◽  
Author(s):  
Baijun Tian
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
Tropical Precipitation ◽  
Double Itcz ◽  
Intercomparison Project ◽  
Fully Coupled

<p>The double-Intertropical Convergence Zone (ITCZ) bias is one of the most outstanding problems in climate models. This study seeks to examine the double-ITCZ bias in the latest state-of-the-art fully coupled global climate models that participated in Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) in comparison to their previous generations (CMIP3 and CMIP5 models). To that end, we have analyzed the long-term annual mean tropical precipitation distributions and several precipitation bias indices that quantify the double-ITCZ biases in 75 climate models including 24 CMIP3 models, 25 CMIP3 models, and 26 CMIP6 models. We find that the double-ITCZ bias and its big inter-model spread persist in CMIP6 models but the double-ITCZ bias is slightly reduced from CMIP3 or CMIP5 models to CMIP6 models.</p>

scholarly journals The Pliocene Model Intercomparison Project Phase 2: large-scale climate features and climate sensitivity

2020 ◽  
Vol 16 (6) ◽  
pp. 2095-2123 ◽  
Author(s):  
Alan M. Haywood ◽  
Julia C. Tindall ◽  
Harry J. Dowsett ◽  
Aisling M. Dolan ◽  
Kevin M. Foley ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Large Scale ◽  
Climate Models ◽  
Temperature Response ◽  
Surface Air Temperature ◽  
System Response ◽  
Phase 2 ◽  
Model Intercomparison ◽  
Intergovernmental Panel ◽  
Intercomparison Project

Abstract. The Pliocene epoch has great potential to improve our understanding of the long-term climatic and environmental consequences of an atmospheric CO2 concentration near ∼400 parts per million by volume. Here we present the large-scale features of Pliocene climate as simulated by a new ensemble of climate models of varying complexity and spatial resolution based on new reconstructions of boundary conditions (the Pliocene Model Intercomparison Project Phase 2; PlioMIP2). As a global annual average, modelled surface air temperatures increase by between 1.7 and 5.2 ∘C relative to the pre-industrial era with a multi-model mean value of 3.2 ∘C. Annual mean total precipitation rates increase by 7 % (range: 2 %–13 %). On average, surface air temperature (SAT) increases by 4.3 ∘C over land and 2.8 ∘C over the oceans. There is a clear pattern of polar amplification with warming polewards of 60∘ N and 60∘ S exceeding the global mean warming by a factor of 2.3. In the Atlantic and Pacific oceans, meridional temperature gradients are reduced, while tropical zonal gradients remain largely unchanged. There is a statistically significant relationship between a model's climate response associated with a doubling in CO2 (equilibrium climate sensitivity; ECS) and its simulated Pliocene surface temperature response. The mean ensemble Earth system response to a doubling of CO2 (including ice sheet feedbacks) is 67 % greater than ECS; this is larger than the increase of 47 % obtained from the PlioMIP1 ensemble. Proxy-derived estimates of Pliocene sea surface temperatures are used to assess model estimates of ECS and give an ECS range of 2.6–4.8 ∘C. This result is in general accord with the ECS range presented by previous Intergovernmental Panel on Climate Change (IPCC) Assessment Reports.

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>

Trends in the CERES Dataset, 2000–13: The Effects of Sea Ice and Jet Shifts and Comparison to Climate Models

2014 ◽  
Vol 27 (6) ◽  
pp. 2444-2456 ◽  
Author(s):  
Dennis L. Hartmann ◽  
Paulo Ceppi
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Climate Models ◽  
Radiant Energy ◽  
Shortwave Radiation ◽  
Radiative Energy ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Sea Ice Extent ◽  
Intercomparison Project

Abstract The Clouds and the Earth’s Radiant Energy System (CERES) observations of global top-of-atmosphere radiative energy fluxes for the period March 2000–February 2013 are examined for robust trends and variability. The trend in Arctic ice is clearly evident in the time series of reflected shortwave radiation, which closely follows the record of ice extent. The data indicate that, for every 106 km2 decrease in September sea ice extent, annual-mean absorbed solar radiation averaged over 75°–90°N increases by 2.5 W m−2, or about 6 W m−2 between 2000 and 2012. CMIP5 models generally show a much smaller change in sea ice extent over the 1970–2012 period, but the relationship of sea ice extent to reflected shortwave is in good agreement with recent observations. Another robust trend during this period is an increase in reflected shortwave radiation in the zonal belt from 45° to 65°S. This trend is mostly related to increases in sea ice concentrations in the Southern Ocean and less directly related to cloudiness trends associated with the annular variability of the Southern Hemisphere. Models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) produce a scaling of cloud reflection to zonal wind increase that is similar to trend observations in regions separated from the direct effects of sea ice. Atmospheric Model Intercomparison Project (AMIP) model responses over the Southern Ocean are not consistent with each other or with the observed shortwave trends in regions removed from the direct effect of sea ice.

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 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.

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