Search Results
You are looking at 21 - 26 of 26 items for :
- Author or Editor: David J. Karoly x
- Article x
- Refine by Access: Content accessible to me x
Abstract
The skill of eight climate models in simulating the variability and trends in the observed areal extent of daily temperature and precipitation extremes is evaluated across five large-scale regions, using the climate extremes index (CEI) framework. Focusing on Europe, North America, Asia, Australia, and the Northern Hemisphere, results show that overall the models are generally able to simulate the decadal variability and trends of the observed temperature and precipitation components over the period 1951–2005. Climate models are able to reproduce observed increasing trends in the area experiencing warm maximum and minimum temperature extremes, as well as, to a lesser extent, increasing trends in the areas experiencing an extreme contribution of heavy precipitation to total annual precipitation for the Northern Hemisphere regions. Using simulations performed under different radiative forcing scenarios, the causes of simulated and observed trends are investigated. A clear anthropogenic signal is found in the trends in the maximum and minimum temperature components for all regions. In North America, a strong anthropogenically forced trend in the maximum temperature component is simulated despite no significant trend in the gridded observations, although a trend is detected in a reanalysis product. A distinct anthropogenic influence is also found for trends in the area affected by a much-above-average contribution of heavy precipitation to annual precipitation totals for Europe in a majority of models and to varying degrees in other Northern Hemisphere regions. However, observed trends in the area experiencing extreme total annual precipitation and extreme number of wet and dry days are not reproduced by climate models under any forcing scenario.
Abstract
The skill of eight climate models in simulating the variability and trends in the observed areal extent of daily temperature and precipitation extremes is evaluated across five large-scale regions, using the climate extremes index (CEI) framework. Focusing on Europe, North America, Asia, Australia, and the Northern Hemisphere, results show that overall the models are generally able to simulate the decadal variability and trends of the observed temperature and precipitation components over the period 1951–2005. Climate models are able to reproduce observed increasing trends in the area experiencing warm maximum and minimum temperature extremes, as well as, to a lesser extent, increasing trends in the areas experiencing an extreme contribution of heavy precipitation to total annual precipitation for the Northern Hemisphere regions. Using simulations performed under different radiative forcing scenarios, the causes of simulated and observed trends are investigated. A clear anthropogenic signal is found in the trends in the maximum and minimum temperature components for all regions. In North America, a strong anthropogenically forced trend in the maximum temperature component is simulated despite no significant trend in the gridded observations, although a trend is detected in a reanalysis product. A distinct anthropogenic influence is also found for trends in the area affected by a much-above-average contribution of heavy precipitation to annual precipitation totals for Europe in a majority of models and to varying degrees in other Northern Hemisphere regions. However, observed trends in the area experiencing extreme total annual precipitation and extreme number of wet and dry days are not reproduced by climate models under any forcing scenario.
Abstract
Increasing surface temperatures are expected to result in longer growing seasons. An optimal detection analysis is carried out to assess the significance of increases in the growing season length during 1950–99, and to measure the anthropogenic component of the change. The signal is found to be detectable, both on global and continental scales, and human influence needs to be accounted for if it is to be fully explained. The change in the growing season length is found to be asymmetric and largely due to the earlier onset of spring, rather than the later ending of autumn. The growing season length, based on exceedence of local temperature thresholds, has a rate of increase of about 1.5 days decade−1 over the observation area. Local variations also allow for negative trends in parts of North America. The analysis suggests that the signal can be attributed to the anthropogenic forcings that have acted on the climate system and no other forcings are necessary to describe the change. Model projections predict that under future climate change the later ending of autumn will also contribute significantly to the lengthening of the growing season, which will increase in the twenty-first century by more than a month. Such major changes in seasonality will affect physical and biological systems in several ways, leading to important environmental and socioeconomic consequences and adaptation challenges.
Abstract
Increasing surface temperatures are expected to result in longer growing seasons. An optimal detection analysis is carried out to assess the significance of increases in the growing season length during 1950–99, and to measure the anthropogenic component of the change. The signal is found to be detectable, both on global and continental scales, and human influence needs to be accounted for if it is to be fully explained. The change in the growing season length is found to be asymmetric and largely due to the earlier onset of spring, rather than the later ending of autumn. The growing season length, based on exceedence of local temperature thresholds, has a rate of increase of about 1.5 days decade−1 over the observation area. Local variations also allow for negative trends in parts of North America. The analysis suggests that the signal can be attributed to the anthropogenic forcings that have acted on the climate system and no other forcings are necessary to describe the change. Model projections predict that under future climate change the later ending of autumn will also contribute significantly to the lengthening of the growing season, which will increase in the twenty-first century by more than a month. Such major changes in seasonality will affect physical and biological systems in several ways, leading to important environmental and socioeconomic consequences and adaptation challenges.
Abstract
This study applies a multiphase, multiple-rheology, scalable, and extensible geofluid model to the Greenland Ice Sheet (GrIS). The model is driven by monthly atmospheric forcing from global climate model simulations. Novel features of the model, referred to as the scalable and extensible geofluid modeling system (SEGMENT-Ice), include using the full Navier–Stokes equations to account for nonlocal dynamic balance and its influence on ice flow, and a granular sliding layer between the bottom ice layer and the lithosphere layer to provide a mechanism for possible large-scale surges in a warmer future climate (granular basal layer is for certain specific regions, though). Monthly climate of SEGMENT-Ice allows an investigation of detailed features such as seasonal melt area extent (SME) over Greenland. The model reproduced reasonably well the annual maximum SME and total ice mass lost rate when compared observations from the Special Sensing Microwave Imager (SSM/I) and Gravity Recovery and Climate Experiment (GRACE) over the past few decades.
The SEGMENT-Ice simulations are driven by projections from two relatively high-resolution climate models, the NCAR Community Climate System Model, version 3 (CCSM3) and the Model for Interdisciplinary Research on Climate 3.2, high-resolution version [MIROC3.2(hires)], under a realistic twenty-first-century greenhouse gas emission scenario. They suggest that the surface flow would be enhanced over the entire GrIS owing to a reduction of ice viscosity as the temperature increases, despite the small change in the ice surface topography over the interior of Greenland. With increased surface flow speed, strain heating induces more rapid heating in the ice at levels deeper than due to diffusion alone. Basal sliding, especially for granular sediments, provides an efficient mechanism for fast-glacier acceleration and enhanced mass loss. This mechanism, absent from other models, provides a rapid dynamic response to climate change. Net mass loss estimates from the new model should reach ~220 km3 yr−1 by 2100, significantly higher than estimates by the Intergovernmental Panel on Climate Change (IPCC) Assessment Report 4 (AR4) of ~50–100 km3 yr−1. By 2100, the perennial frozen surface area decreases up to ~60%, to ~7 × 105 km2, indicating a massive expansion of the ablation zone. Ice mass change patterns, particularly along the periphery, are very similar between the two climate models.
Abstract
This study applies a multiphase, multiple-rheology, scalable, and extensible geofluid model to the Greenland Ice Sheet (GrIS). The model is driven by monthly atmospheric forcing from global climate model simulations. Novel features of the model, referred to as the scalable and extensible geofluid modeling system (SEGMENT-Ice), include using the full Navier–Stokes equations to account for nonlocal dynamic balance and its influence on ice flow, and a granular sliding layer between the bottom ice layer and the lithosphere layer to provide a mechanism for possible large-scale surges in a warmer future climate (granular basal layer is for certain specific regions, though). Monthly climate of SEGMENT-Ice allows an investigation of detailed features such as seasonal melt area extent (SME) over Greenland. The model reproduced reasonably well the annual maximum SME and total ice mass lost rate when compared observations from the Special Sensing Microwave Imager (SSM/I) and Gravity Recovery and Climate Experiment (GRACE) over the past few decades.
The SEGMENT-Ice simulations are driven by projections from two relatively high-resolution climate models, the NCAR Community Climate System Model, version 3 (CCSM3) and the Model for Interdisciplinary Research on Climate 3.2, high-resolution version [MIROC3.2(hires)], under a realistic twenty-first-century greenhouse gas emission scenario. They suggest that the surface flow would be enhanced over the entire GrIS owing to a reduction of ice viscosity as the temperature increases, despite the small change in the ice surface topography over the interior of Greenland. With increased surface flow speed, strain heating induces more rapid heating in the ice at levels deeper than due to diffusion alone. Basal sliding, especially for granular sediments, provides an efficient mechanism for fast-glacier acceleration and enhanced mass loss. This mechanism, absent from other models, provides a rapid dynamic response to climate change. Net mass loss estimates from the new model should reach ~220 km3 yr−1 by 2100, significantly higher than estimates by the Intergovernmental Panel on Climate Change (IPCC) Assessment Report 4 (AR4) of ~50–100 km3 yr−1. By 2100, the perennial frozen surface area decreases up to ~60%, to ~7 × 105 km2, indicating a massive expansion of the ablation zone. Ice mass change patterns, particularly along the periphery, are very similar between the two climate models.
Abstract
The time–frequency spectral structure of El Niño–Southern Oscillation (ENSO) time series holds much information about the physical dynamics of the ENSO system. The authors have analyzed changes of the spectrum with time of three ENSO indices: the conventional Southern Oscillation index (SOI), Niño3 sea surface temperatures, and a tropical Pacific rain index, over the period 1871–1995. Three methods of time–frequency analysis—windowed Fourier transform, wavelet analysis, and windowed Prony’s method—were used, and the results are in good agreement. The time–frequency spectra of all the series show strong multidecadal variations over the past century. In particular, there was reduced activity of ENSO in the 2–3-yr periodicity range during the period 1920–60, compared with both the earlier and later periods. The dominant frequencies in the spectra do not appear to be constrained to certain frequency bands, and there is no evidence that the ENSO system has fixed modes of oscillation.
The qualitative behavior of the real SOI time series has been compared with that of time series simulated by an autoregressive stochastic process of order 3 and time series created by phase-randomizing the spectral components of the SOI. The decadal variability of the amplitude and time–frequency spectra was found to be very similar between the observed and simulated SOIs. This suggests that the decadal variability of ENSO can be well simulated by a stochastic model and that stochastic forcing may be an important component of ENSO dynamics.
Abstract
The time–frequency spectral structure of El Niño–Southern Oscillation (ENSO) time series holds much information about the physical dynamics of the ENSO system. The authors have analyzed changes of the spectrum with time of three ENSO indices: the conventional Southern Oscillation index (SOI), Niño3 sea surface temperatures, and a tropical Pacific rain index, over the period 1871–1995. Three methods of time–frequency analysis—windowed Fourier transform, wavelet analysis, and windowed Prony’s method—were used, and the results are in good agreement. The time–frequency spectra of all the series show strong multidecadal variations over the past century. In particular, there was reduced activity of ENSO in the 2–3-yr periodicity range during the period 1920–60, compared with both the earlier and later periods. The dominant frequencies in the spectra do not appear to be constrained to certain frequency bands, and there is no evidence that the ENSO system has fixed modes of oscillation.
The qualitative behavior of the real SOI time series has been compared with that of time series simulated by an autoregressive stochastic process of order 3 and time series created by phase-randomizing the spectral components of the SOI. The decadal variability of the amplitude and time–frequency spectra was found to be very similar between the observed and simulated SOIs. This suggests that the decadal variability of ENSO can be well simulated by a stochastic model and that stochastic forcing may be an important component of ENSO dynamics.
Abstract
The stationarity of relationships between local and remote climates is a necessary, yet implicit, assumption underlying many paleoclimate reconstructions. However, the assumption is tenuous for many seasonal relationships between interannual variations in the El Niño–Southern Oscillation (ENSO) and the southern annular mode (SAM) and Australasian precipitation and mean temperatures. Nonstationary statistical relationships between local and remote climates on the 31–71-yr time scale, defined as a change in their strength and/or phase outside that expected from local climate noise, are detected on near-centennial time scales from instrumental data, climate model simulations, and paleoclimate proxies.
The relationships between ENSO and SAM and Australasian precipitation were nonstationary at 21%–37% of Australasian stations from 1900 to 2009 and strongly covaried, suggesting common modulation. Control simulations from three coupled climate models produce ENSO-like and SAM-like patterns of variability, but differ in detail to the observed patterns in Australasia. However, the model teleconnections also display nonstationarity, in some cases for over 50% of the domain. Therefore, nonstationary local–remote climatic relationships are inherent in environments regulated by internal variability. The assessments using paleoclimate reconstructions are not robust because of extraneous noise associated with the paleoclimate proxies.
Instrumental records provide the only means of calibrating and evaluating regional paleoclimate reconstructions. However, the length of Australasian instrumental observations may be too short to capture the near-centennial-scale variations in local–remote climatic relationships, potentially compromising these reconstructions. The uncertainty surrounding nonstationary teleconnections must be acknowledged and quantified. This should include interpreting nonstationarities in paleoclimate reconstructions using physically based frameworks.
Abstract
The stationarity of relationships between local and remote climates is a necessary, yet implicit, assumption underlying many paleoclimate reconstructions. However, the assumption is tenuous for many seasonal relationships between interannual variations in the El Niño–Southern Oscillation (ENSO) and the southern annular mode (SAM) and Australasian precipitation and mean temperatures. Nonstationary statistical relationships between local and remote climates on the 31–71-yr time scale, defined as a change in their strength and/or phase outside that expected from local climate noise, are detected on near-centennial time scales from instrumental data, climate model simulations, and paleoclimate proxies.
The relationships between ENSO and SAM and Australasian precipitation were nonstationary at 21%–37% of Australasian stations from 1900 to 2009 and strongly covaried, suggesting common modulation. Control simulations from three coupled climate models produce ENSO-like and SAM-like patterns of variability, but differ in detail to the observed patterns in Australasia. However, the model teleconnections also display nonstationarity, in some cases for over 50% of the domain. Therefore, nonstationary local–remote climatic relationships are inherent in environments regulated by internal variability. The assessments using paleoclimate reconstructions are not robust because of extraneous noise associated with the paleoclimate proxies.
Instrumental records provide the only means of calibrating and evaluating regional paleoclimate reconstructions. However, the length of Australasian instrumental observations may be too short to capture the near-centennial-scale variations in local–remote climatic relationships, potentially compromising these reconstructions. The uncertainty surrounding nonstationary teleconnections must be acknowledged and quantified. This should include interpreting nonstationarities in paleoclimate reconstructions using physically based frameworks.
Decadal Prediction
Can It Be Skillful?
A new field of study, “decadal prediction,” is emerging in climate science. Decadal prediction lies between seasonal/interannual forecasting and longer-term climate change projections, and focuses on time-evolving regional climate conditions over the next 10–30 yr. Numerous assessments of climate information user needs have identified this time scale as being important to infrastructure planners, water resource managers, and many others. It is central to the information portfolio required to adapt effectively to and through climatic changes. At least three factors influence time-evolving regional climate at the decadal time scale: 1) climate change commitment (further warming as the coupled climate system comes into adjustment with increases of greenhouse gases that have already occurred), 2) external forcing, particularly from future increases of greenhouse gases and recovery of the ozone hole, and 3) internally generated variability. Some decadal prediction skill has been demonstrated to arise from the first two of these factors, and there is evidence that initialized coupled climate models can capture mechanisms of internally generated decadal climate variations, thus increasing predictive skill globally and particularly regionally. Several methods have been proposed for initializing global coupled climate models for decadal predictions, all of which involve global time-evolving three-dimensional ocean data, including temperature and salinity. An experimental framework to address decadal predictability/prediction is described in this paper and has been incorporated into the coordinated Coupled Model Intercomparison Model, phase 5 (CMIP5) experiments, some of which will be assessed for the IPCC Fifth Assessment Report (AR5). These experiments will likely guide work in this emerging field over the next 5 yr.
A new field of study, “decadal prediction,” is emerging in climate science. Decadal prediction lies between seasonal/interannual forecasting and longer-term climate change projections, and focuses on time-evolving regional climate conditions over the next 10–30 yr. Numerous assessments of climate information user needs have identified this time scale as being important to infrastructure planners, water resource managers, and many others. It is central to the information portfolio required to adapt effectively to and through climatic changes. At least three factors influence time-evolving regional climate at the decadal time scale: 1) climate change commitment (further warming as the coupled climate system comes into adjustment with increases of greenhouse gases that have already occurred), 2) external forcing, particularly from future increases of greenhouse gases and recovery of the ozone hole, and 3) internally generated variability. Some decadal prediction skill has been demonstrated to arise from the first two of these factors, and there is evidence that initialized coupled climate models can capture mechanisms of internally generated decadal climate variations, thus increasing predictive skill globally and particularly regionally. Several methods have been proposed for initializing global coupled climate models for decadal predictions, all of which involve global time-evolving three-dimensional ocean data, including temperature and salinity. An experimental framework to address decadal predictability/prediction is described in this paper and has been incorporated into the coordinated Coupled Model Intercomparison Model, phase 5 (CMIP5) experiments, some of which will be assessed for the IPCC Fifth Assessment Report (AR5). These experiments will likely guide work in this emerging field over the next 5 yr.
