scholarly journals Attributing Historical and Future Evolution of Radiative Feedbacks to Regional Warming Patterns using a Green’s Function Approach: The Preeminence of the Western Pacific

2019 ◽  
Vol 32 (17) ◽  
pp. 5471-5491 ◽  
Author(s):  
Yue Dong ◽  
Cristian Proistosescu ◽  
Kyle C. Armour ◽  
David S. Battisti
Keyword(s):  
Green’S Function ◽  
Green's Function ◽  
Climate Sensitivity ◽  
Climate Model ◽  
Global Climate Model ◽  
Western Pacific ◽  
Surface Warming ◽  
The Future ◽  
Radiative Feedbacks ◽  
The Western Pacific

Abstract Global radiative feedbacks have been found to vary in global climate model (GCM) simulations. Atmospheric GCMs (AGCMs) driven with historical patterns of sea surface temperatures (SSTs) and sea ice concentrations produce radiative feedbacks that trend toward more negative values, implying low climate sensitivity, over recent decades. Freely evolving coupled GCMs driven by increasing CO2 produce radiative feedbacks that trend toward more positive values, implying increasing climate sensitivity, in the future. While this time variation in feedbacks has been linked to evolving SST patterns, the role of particular regions has not been quantified. Here, a Green’s function is derived from a suite of simulations within an AGCM (NCAR’s CAM4), allowing an attribution of global feedback changes to surface warming in each region. The results highlight the radiative response to surface warming in ascent regions of the western tropical Pacific as the dominant control on global radiative feedback changes. Historical warming from the 1950s to 2000s preferentially occurred in the western Pacific, yielding a strong global outgoing radiative response at the top of the atmosphere (TOA) and thus a strongly negative global feedback. Long-term warming in coupled GCMs occurs preferentially in tropical descent regions and in high latitudes, where surface warming yields small global TOA radiation change but large global surface air temperature change, and thus a less-negative global feedback. These results illuminate the importance of determining mechanisms of warm pool warming for understanding how feedbacks have varied historically and will evolve in the future.

How do polar clouds and precipitation affect global warming?

2021 ◽  
Author(s):  
Jennifer Kay
Keyword(s):  
Global Warming ◽  
Climate Model ◽  
Research Problem ◽  
Cloud Feedback ◽  
Polar Regions ◽  
Unsolved Research Problem ◽  
Cloud Feedbacks ◽  
Surface Warming ◽  
Polar Clouds ◽  
Radiative Feedbacks

<p>Understanding the influence of clouds and precipitation on global warming remains an important unsolved research problem. This talk presents an overview of this topic, with a focus on recent observations, theory, and modeling results for polar clouds. After a general introduction, experiments that disable cloud radiative feedbacks or “lock the clouds” within a state‐of‐the‐art,  well‐documented, and observationally vetted climate model will be presented. Through comparison of idealized greenhouse warming experiments with and without cloud locking, the sign and magnitude cloud feedbacks can be quantified. Global cloud feedbacks increase both global and Arctic warming by around 25%. In contrast, disabling Arctic cloud feedbacks has a negligible influence on both Arctic and global surface warming. Do observations and theory support a positive global cloud feedback and a weak Arctic cloud feedback?  How does precipitation affect polar cloud feedbacks? What are the implications especially for climate change in polar regions?  </p>

Statistical Projection of the North Atlantic Storm Tracks

2019 ◽  
Vol 58 (7) ◽  
pp. 1509-1522 ◽  
Author(s):  
Kajsa M. Parding ◽  
Rasmus Benestad ◽  
Abdelkader Mezghani ◽  
Helene B. Erlandsen
Keyword(s):  
North Atlantic ◽  
Geopotential Height ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Storm Tracks ◽  
Cmip5 Models ◽  
The North ◽  
The Future ◽  
The North Atlantic

AbstractA method for empirical–statistical downscaling was adapted to project seasonal cyclone density over the North Atlantic Ocean. To this aim, the seasonal mean cyclone density was derived from instantaneous values of the 6-h mean sea level pressure (SLP) reanalysis fields. The cyclone density was then combined with seasonal mean reanalysis and global climate model projections of SLP or 500-hPa geopotential height to obtain future projections of the North Atlantic storm tracks. The empirical–statistical approach is computationally efficient because it makes use of seasonally aggregated cyclone statistics and allows the future cyclone density to be estimated from the full ensemble of available CMIP5 models rather than from a smaller subset. However, the projected cyclone density in the future differs considerably depending on the choice of predictor, SLP, or 500-hPa geopotential height. This discrepancy suggests that the relationship between the cyclone density and SLP, 500-hPa geopotential height, or both is nonstationary; that is, that the statistical model depends on the calibration period. A stationarity test based on 6-hourly HadGEM2-ES data indicated that the 500-hPa geopotential height was not a robust predictor of cyclone density.

scholarly journals Quantification of the Expected Changes in Annual Maximum Daily Precipitation Quantiles under Climate Change in the Iberian Peninsula

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 23 ◽  
Author(s):  
Carlos Garijo ◽  
Luis Mediero
Keyword(s):  
Climate Change ◽  
Iberian Peninsula ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Control Period ◽  
Regional Climate Models ◽  
The Future

Climate model projections can be used to assess the expected behaviour of extreme precipitations in the future due to climate change. The European part of the Coordinated Regional Climate Downscalling Experiment (EURO-CORDEX) provides precipitation projections for the future under various representative concentration pathways (RCPs) through regionalised Global Climate Model (GCM) outputs by a set of Regional Climate Models (RCMs). In this work, 12 combinations of GCM and RCM under two scenarios (RCP 4.5 and RCP 8.5) supplied by the EURO-CORDEX are analysed for the Iberian Peninsula. Precipitation quantiles for a set of probabilities of non-exceedance are estimated by using the Generalized Extreme Value (GEV) distribution and L-moments. Precipitation quantiles expected in the future are compared with the precipitation quantiles in the control period for each climate model. An approach based on Monte Carlo simulations is developed in order to assess the uncertainty from the climate model projections. Expected changes in the future are compared with the sampling uncertainty in the control period. Thus, statistically significant changes are identified. The higher the significance threshold, the fewer cells with significant changes are identified. Consequently, a set of maps are obtained in order to assist the decision-making process in subsequent climate change studies.

scholarly journals An Investigation of the Connections among Convection, Clouds, and Climate Sensitivity in a Global Climate Model

2014 ◽  
Vol 27 (5) ◽  
pp. 1845-1862 ◽  
Author(s):  
Ming Zhao
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Convective Precipitation ◽  
Entrainment Rate ◽  
Cloud Radiative Forcing ◽  
Precipitation Efficiency ◽  
Perturbed Physics

Abstract This study explores connections between process-level modeling of convection and global climate model (GCM) simulated clouds and cloud feedback to global warming through a set of perturbed-physics and perturbed sea surface temperature experiments. A bulk diagnostic approach is constructed, and a set of variables is derived and demonstrated to be useful in understanding the simulated relationship. In particular, a novel bulk quantity, the convective precipitation efficiency or equivalently the convective detrainment efficiency, is proposed as a simple measure of the aggregated properties of parameterized convection important to the GCM simulated clouds. As the convective precipitation efficiency increases in the perturbed-physics experiments, both liquid and ice water path decrease, with low and middle cloud fractions diminishing at a faster rate than high cloud fractions. This asymmetry results in a large sensitivity of top-of-atmosphere net cloud radiative forcing to changes in convective precipitation efficiency in this limited set of models. For global warming experiments, intermodel variations in the response of cloud condensate, low cloud fraction, and total cloud radiative forcing are well explained by model variations in response to total precipitation (or detrainment) efficiency. Despite significant variability, all of the perturbed-physics models produce a sizable increase in precipitation efficiency to warming. A substantial fraction of the increase is due to its convective component, which depends on the parameterization of cumulus mixing and convective microphysical processes. The increase in convective precipitation efficiency and associated change in convective cloud height distribution owing to warming explains the increased cloud feedback and climate sensitivity in recently developed Geophysical Fluid Dynamics Laboratory GCMs. The results imply that a cumulus scheme using fractional removal of condensate for precipitation and inverse calculation of the entrainment rate tends to produce a lower climate sensitivity than a scheme using threshold removal for precipitation and the entrainment rate formulated inversely dependent on convective depth.

scholarly journals Changes in Climate Extremes over North Thailand, 1960–2099

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Mohammad Badrul Masud ◽  
Peeyush Soni ◽  
Sangam Shrestha ◽  
Nitin K. Tripathi
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Annual Rainfall ◽  
Trend Test ◽  
Daily Maximum ◽  
Climate Extreme ◽  
The Future ◽  
North Thailand ◽  
Extreme Indices

This study analyzes 24 climate extreme indices over North Thailand using observed data for daily maximum and minimum temperatures and total daily rainfall for the 1960–2010 period, and HadCM3 Global Climate Model (GCM) and PRECIS Regional Climate Model simulated data for the 1960–2100 period. A statistical downscaling tool is employed to downscale GCM outputs. Variations in and trends of historical and future climates are identified using the nonparametric Mann-Kendall trend test and Sen’s slope. Temperature extreme indices showed a significant rising trend during the observed period and are expected to increase significantly with an increase in summer days and tropical nights in the future. A notable decline in the number of cool days and nights is also expected in the study area while the number of warm days and nights is expected to increase. There was an insignificant decrease in total annual rainfall, number of days with rainfall more than 10 and 20 mm. However, the annual rainfall is projected to increase by 9.65% in the future 2011–2099 period compared to the observed 1960–2010 period.

Will the western Pacific subtropical high constantly intensify in the future?

2015 ◽  
Vol 47 (1-2) ◽  
pp. 567-577 ◽  
Author(s):  
Yanyan Huang ◽  
Xiaofan Li ◽  
Huijun Wang
Keyword(s):  
Western Pacific Subtropical High ◽  
Western Pacific ◽  
Subtropical High ◽  
The Future ◽  
The Western Pacific

scholarly journals Probabilistic projections of future warming and climate sensitivity trajectories

2021 ◽  
Author(s):  
Philip Goodwin
Keyword(s):  
Global Warming ◽  
Climate Sensitivity ◽  
Climate Model ◽  
Heat Content ◽  
Surface Warming ◽  
Observational Uncertainty ◽  
Future Global Warming ◽  
Intercomparison Project ◽  
Future Warming ◽  
Probabilistic Projections

Abstract Projections of future global mean surface warming for a given forcing scenario remain uncertain, largely due to uncertainty in the climate sensitivity. The ensemble of Earth system models from the Climate Model Intercomparison Project phase 6 (CMIP6) represent the dominant tools for projecting future global warming. However, the distribution of climate sensitivities within the CMIP6 ensemble is not representative of recent independent probabilistic estimates, and the ensemble contains significant variation in simulated historic surface warming outside agreement with observational datasets. Here, a Bayesian approach is used to infer joint probabilistic projections of future surface warming and climate sensitivity for SSP scenarios. The projections use an efficient climate model ensemble filtered and weighted to encapsulate observational uncertainty in historic warming and ocean heat content anomalies. The probabilistic projection of climate sensitivity produces a best estimate of 2.9 °C, and 5th to 95th percentile range of 1.5 to 4.6 °C, in line with previous estimates using multiple lines of evidence. The joint projection of surface warming over the period 2030 to 2040 has a 50% or greater probability of exceeding 1.5 °C above preindustrial for all SSPs considered: 119, 126, 245, 370 and 585. Average warming by the period 2050 to 2060 has a greater than 50% chance of exceeding 2 °C for SSPs 245, 370 and 585. These results imply that global warming is no longer likely to remain under 1.5 °C, even with drastic and immediate mitigation, and highlight the importance of urgent action to avoid exceeding 2 °C warming.

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