scholarly journals A Statistical Downscaling Model for Southern Australia Winter Rainfall

2009 ◽  
Vol 22 (5) ◽  
pp. 1142-1158 ◽  
Author(s):  
Yun Li ◽  
Ian Smith
Keyword(s):  
Climate Model ◽  
Close Association ◽  
Large Fraction ◽  
Statistical Downscaling Model ◽  
Winter Rainfall ◽  
Sea Level Pressure ◽  
Model Simulations ◽  
Rainfall Anomalies ◽  
Climate Model Simulations ◽  
Emissions Scenario

Abstract A technique for obtaining downscaled rainfall projections from climate model simulations is described. This technique makes use of the close association between mean sea level pressure (MSLP) patterns and rainfall over southern Australia during winter. Principal components of seasonal mean MSLP anomalies are linked to observed rainfall anomalies at regional, gridpoint, and point scales. A maximum of four components is sufficient to capture a relatively large fraction of the observed variance in rainfall at most locations. These are used to interpret the MSLP patterns from a single climate model, which has been used to simulate both present-day and future climate. The resulting downscaled values provide 1) a closer representation of the observed present-day rainfall than the raw climate model values and 2) alternative estimates of future changes to rainfall that arise owing to changes in mean MSLP. While decreases are simulated for later this century (under a single emissions scenario), the downscaled values, in percentage terms, tend to be less.

scholarly journals On the Choice of Ensemble Mean for Estimating the Forced Signal in the Presence of Internal Variability

2018 ◽  
Vol 31 (14) ◽  
pp. 5681-5693 ◽  
Author(s):  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
Jules B. Kajtar ◽  
Michael E. Mann ◽  
Byron A. Steinman
Keyword(s):  
Regional Differences ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Single Model ◽  
Sea Level Pressure ◽  
Model Simulations ◽  
Ensemble Mean ◽  
Climate Model Simulations ◽  
The Impact

Abstract In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences.

AN ASSEMBLY OF PRESCRIBED BOUNDARY CONDITIONS FOR THE CLIMATE-MODEL SIMULATIONS ACROSS THE PERMO-TRIASSIC BOUNDARY(PTB)

2020 ◽  
Author(s):  
Mitali D. Gautam ◽  
◽  
Arne Winguth ◽  
Cornelia Winguth ◽  
Christopher Scotese ◽  
...  
Keyword(s):  
Boundary Conditions ◽  
Climate Model ◽  
Triassic Boundary ◽  
Model Simulations ◽  
Climate Model Simulations

scholarly journals Anthropogenic forcing and response yield observed positive trend in Earth’s energy imbalance

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shiv Priyam Raghuraman ◽  
David Paynter ◽  
V. Ramaswamy
Keyword(s):  
Radiative Forcing ◽  
Climate Model ◽  
Solar Spectrum ◽  
Internal Variability ◽  
Satellite Observations ◽  
Positive Trend ◽  
Energy Imbalance ◽  
Model Simulations ◽  
Heat Uptake ◽  
Climate Model Simulations

AbstractThe observed trend in Earth’s energy imbalance (TEEI), a measure of the acceleration of heat uptake by the planet, is a fundamental indicator of perturbations to climate. Satellite observations (2001–2020) reveal a significant positive globally-averaged TEEI of 0.38 ± 0.24 Wm−2decade−1, but the contributing drivers have yet to be understood. Using climate model simulations, we show that it is exceptionally unlikely (<1% probability) that this trend can be explained by internal variability. Instead, TEEI is achieved only upon accounting for the increase in anthropogenic radiative forcing and the associated climate response. TEEI is driven by a large decrease in reflected solar radiation and a small increase in emitted infrared radiation. This is because recent changes in forcing and feedbacks are additive in the solar spectrum, while being nearly offset by each other in the infrared. We conclude that the satellite record provides clear evidence of a human-influenced climate system.

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