Bootstrap estimated seasonal potential predictability of global temperature and precipitation

2011 ◽  
Vol 38 (7) ◽  
pp. n/a-n/a ◽  
Author(s):  
X. Feng ◽  
T. DelSole ◽  
P. Houser
Keyword(s):  
Global Temperature ◽  
Potential Predictability ◽  
Temperature And Precipitation ◽  
Seasonal Potential Predictability

Identifying the sources of seasonal potential predictability using Fractional Integral Statistical Model with a Variance Decomposition Method

2021 ◽  
Author(s):  
Da Nian ◽  
Naiming Yuan ◽  
Kairan Ying ◽  
Ge Liu ◽  
Zuntao Fu ◽  
...  
Keyword(s):  
Statistical Model ◽  
Decomposition Method ◽  
Fractional Integral ◽  
Variance Decomposition ◽  
Climate System ◽  
Potential Predictability ◽  
External Forcings ◽  
Multiple Processes ◽  
Temperature Records ◽  
Seasonal Potential Predictability

<p>It is well recognized that climate predictability has three origins: (i)climate memory (inertia of the climate system) that accumulated from the historical conditions, (ii) responses to external forcings, and (iii) dynamical interactions of multiple processes in the climate system. However, how to systematically identify these predictable sources is still an open question. Here, we combine a recently developed Fractional Integral Statistical Model (FISM) with a Variance Decomposition Method (VDM), to systematically estimate the potential sources of predictability. With FISM, one can extract the memory component from the considered variable. For the residual parts, VDM can then be applied to extract the slow varying covariance matrix, which contains signals related to external forcings and dynamical interactions of multiple processes in climate. To show the improvement of our methodology, we have tested it on realistic data, using monthly temperature observations over China during 1960-2017.  It is found that the climate memory component contributes a large portion of the seasonal predictability in the temperature records. Our results offer the potential for more skillful seasonal predictions compared with the results obtained using FISM or VDM alone.</p>

Short-Term Climate Extremes: Prediction Skill and Predictability

2013 ◽  
Vol 26 (2) ◽  
pp. 512-531 ◽  
Author(s):  
Emily J. Becker ◽  
Huug van den Dool ◽  
Malaquias Peña
Keyword(s):  
North America ◽  
South America ◽  
Surface Temperature ◽  
Global Climate Model ◽  
Prediction Skill ◽  
Short Term ◽  
Potential Predictability ◽  
Atlantic Hurricane ◽  
Temperature And Precipitation ◽  
Main Development

Abstract Forecasts for extremes in short-term climate (monthly means) are examined to understand the current prediction capability and potential predictability. This study focuses on 2-m surface temperature and precipitation extremes over North and South America, and sea surface temperature extremes in the Niño-3.4 and Atlantic hurricane main development regions, using the Climate Forecast System (CFS) global climate model, for the period of 1982–2010. The primary skill measures employed are the anomaly correlation (AC) and root-mean-square error (RMSE). The success rate of forecasts is also assessed using contingency tables. The AC, a signal-to-noise skill measure, is routinely higher for extremes in short-term climate than those when all forecasts are considered. While the RMSE for extremes also rises, especially when skill is inherently low, it is found that the signal rises faster than the noise. Permutation tests confirm that this is not simply an effect of reduced sample size. Both 2-m temperature and precipitation forecasts have higher anomaly correlations in the area of South America than North America; credible skill in precipitation is very low over South America and absent over North America, even for extremes. Anomaly correlations for SST are very high in the Niño-3.4 region, especially for extremes, and moderate to high in the Atlantic hurricane main development region. Prediction skill for forecast extremes is similar to skill for observed extremes. Assessment of the potential predictability under perfect-model assumptions shows that predictability and prediction skill have very similar space–time dependence. While prediction skill is higher in CFS version 2 than in CFS version 1, the potential predictability is not.

Simultaneous stabilization of global temperature and precipitation through cocktail geoengineering

2017 ◽  
Vol 44 (14) ◽  
pp. 7429-7437 ◽  
Author(s):  
Long Cao ◽  
Lei Duan ◽  
Govindasamy Bala ◽  
Ken Caldeira
Keyword(s):  
Global Temperature ◽  
Simultaneous Stabilization ◽  
Temperature And Precipitation

scholarly journals Implications of global warming for the climate of African rainforests

2013 ◽  
Vol 368 (1625) ◽  
pp. 20120298 ◽  
Author(s):  
Rachel James ◽  
Richard Washington ◽  
David P. Rowell
Keyword(s):  
Global Warming ◽  
Amazon Basin ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Temperature ◽  
Congo Basin ◽  
Coupled Models ◽  
Temperature And Precipitation ◽  
Equatorial Africa

African rainforests are likely to be vulnerable to changes in temperature and precipitation, yet there has been relatively little research to suggest how the regional climate might respond to global warming. This study presents projections of temperature and precipitation indices of relevance to African rainforests, using global climate model experiments to identify local change as a function of global temperature increase. A multi-model ensemble and two perturbed physics ensembles are used, one with over 100 members. In the east of the Congo Basin, most models (92%) show a wet signal, whereas in west equatorial Africa, the majority (73%) project an increase in dry season water deficits. This drying is amplified as global temperature increases, and in over half of coupled models by greater than 3% per °C of global warming. Analysis of atmospheric dynamics in a subset of models suggests that this could be partly because of a rearrangement of zonal circulation, with enhanced convection in the Indian Ocean and anomalous subsidence over west equatorial Africa, the Atlantic Ocean and, in some seasons, the Amazon Basin. Further research to assess the plausibility of this and other mechanisms is important, given the potential implications of drying in these rainforest regions.

A quantitative assessment of changes in seasonal potential predictability for the twentieth century

2013 ◽  
Vol 41 (9-10) ◽  
pp. 2697-2709 ◽  
Author(s):  
M. Azhar Ehsan ◽  
In-Sik Kang ◽  
Mansour Almazroui ◽  
M. Adnan Abid ◽  
Fred Kucharski
Keyword(s):  
Twentieth Century ◽  
Quantitative Assessment ◽  
Potential Predictability ◽  
Seasonal Potential Predictability

scholarly journals Changes in Interannual Variability and Decadal Potential Predictability under Global Warming

2009 ◽  
Vol 22 (11) ◽  
pp. 3098-3109 ◽  
Author(s):  
G. J. Boer
Keyword(s):  
Global Warming ◽  
Standard Deviation ◽  
Time Scale ◽  
Climate Model ◽  
Decadal Variability ◽  
Climate Change Scenarios ◽  
Potential Predictability ◽  
Temperature And Precipitation ◽  
Mean Temperature ◽  
The Tropics

Abstract Global warming will result in changes in mean temperature and precipitation distributions and is also expected to affect interannual and longer time-scale internally generated variability as a consequence of changes in climate processes and feedbacks. Multimodel estimates of changes in the variability of annual mean temperature and precipitation and in the variability of decadal potential predictability are investigated based on the collection of coupled climate model simulations in the Coupled Model Intercomparison Project phase 3 (CMIP3) data archive. Pooled, multimodel standard deviations of annual mean temperature and precipitation for the unforced preindustrial control climates of the models show good resemblance to observation-based estimates. The internally generated variability of the unforced climate is compared with that of the warmer conditions for simulations with the B1 and A1B climate change scenarios with forcing stabilized at year 2100 values. The standard deviation of annual mean temperature generally decreases with global warming at extratropical latitudes, with the largest percentage decreases over the oceans and largest percentage increases in the tropics and subtropics, although the magnitudes of these increases are smaller. The standard deviation of annual mean precipitation increases almost everywhere, with larger increases in the tropics. Changes are generally larger for the more strongly forced, warmer A1B scenario than for the B1 scenario. The characterization of decadal variability changes in terms of potential predictability stems from the growing interest in producing forecasts for the next decade or several decades. The potential predictability identifies that fraction of the long time-scale variability that is, at least potentially and with enough information, predictable on decadal time scales. There is a general decrease in the internally generated decadal variability of temperature and its potential predictability in the warmer world. The decrease tends to be largest where the decadal potential predictability of the unforced control climate is largest over the high-latitude oceans. The potential predictability of precipitation is small to begin with and generally decreases further. Therefore, there is a potential decrease in the decadal potential predictability of the internally generated component in a warmer world.

scholarly journals Spatial Coherence and Seasonal Predictability of Monsoon Onset over Indonesia

2009 ◽  
Vol 22 (3) ◽  
pp. 840-850 ◽  
Author(s):  
Vincent Moron ◽  
Andrew W. Robertson ◽  
Rizaldi Boer
Keyword(s):  
Large Scale ◽  
Monsoon Season ◽  
Spatial Coherence ◽  
Local Scale ◽  
Monsoon Onset ◽  
Onset Date ◽  
Spatial Average ◽  
Precipitation Data ◽  
Potential Predictability ◽  
Seasonal Potential Predictability

Abstract The seasonal potential predictability of monsoon onset during the August–December season over Indonesia is studied through analysis of the spatial coherence of daily station rainfall and gridded pentad precipitation data from 1979 to 2005. The onset date, defined using a local agronomic definition, exhibits a seasonal northwest-to-southeast progression from northern and central Sumatra (late August) to Timor (mid-December). South of the equator, interannual variability of the onset date is shown to consist of a spatially coherent large-scale component, together with local-scale noise. The high spatial coherence of onset is similar to that of the September–December seasonal total, while postonset amounts averaged over 15–90 days and September–December amount residuals from large-scale onset show much less spatial coherence, especially across the main islands of monsoonal Indonesia. The cumulative rainfall anomalies exhibit also their largest amplitudes before or near the onset date. This implies that seasonal potential predictability over monsoonal Indonesia during the first part of the austral summer monsoon season is largely associated with monsoon onset, and that there is much less predictability within the rainy season itself. A cross-validated canonical correlation analysis using July sea surface temperatures over the tropical Pacific and Indian Oceans (20°S–20°N, 80°–280°E) as predictors of local-scale onset dates exhibits promising hindcast skill (anomaly correlation of ∼0.80 for the spatial average of standardized rain gauges and ∼0.70 for standardized gridded pentad precipitation data).

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