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Karin L. Gleason
,
Jay H. Lawrimore
,
David H. Levinson
,
Thomas R. Karl
, and
David J. Karoly

Abstract

A revised framework is presented that quantifies observed changes in the climate of the contiguous United States through analysis of a revised version of the U.S. Climate Extremes Index (CEI). The CEI is based on a set of climate extremes indicators that measure the fraction of the area of the United States experiencing extremes in monthly mean surface temperature, daily precipitation, and drought (or moisture surplus). In the revised CEI, auxiliary station data, including recently digitized pre-1948 data, are incorporated to extend it further back in time and to improve spatial coverage. The revised CEI is updated for the period from 1910 to the present in near–real time and is calculated for eight separate seasons, or periods.

Results for the annual revised CEI are similar to those from the original CEI. Observations over the past decade continue to support the finding that the area experiencing much above-normal maximum and minimum temperatures in recent years has been on the rise, with infrequent occurrence of much below- normal mean maximum and minimum temperatures. Conversely, extremes in much below-normal mean maximum and minimum temperatures indicate a decline from about 1910 to 1930. An increasing trend in the area experiencing much above-normal proportion of heavy daily precipitation is observed from about 1950 to the present. A period with a much greater-than-normal number of days without precipitation is also noted from about 1910 to the mid-1930s. Warm extremes in mean maximum and minimum temperature observed during the summer and warm seasons show a more pronounced increasing trend since the mid-1970s. Results from the winter season show large variability in extremes and little evidence of a trend. The cold season CEI indicates an increase in extremes since the early 1970s yet has large multidecadal variability.

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Andrea J. Dittus
,
David J. Karoly
,
Sophie C. Lewis
,
Lisa V. Alexander
, and
Markus G. Donat

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.

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Mandy B. Freund
,
Josephine R. Brown
,
Benjamin J. Henley
,
David J. Karoly
, and
Jaclyn N. Brown

Abstract

Given the consequences and global significance of El Niño–Southern Oscillation (ENSO) events it is essential to understand the representation of El Niño diversity in climate models for the present day and the future. In recent decades, El Niño events have occurred more frequently in the central Pacific (CP). Eastern Pacific (EP) El Niño events have increased in intensity. However, the processes and future implications of these observed changes in El Niño are not well understood. Here, the frequency and intensity of El Niño events are assessed in models from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6), and results are compared to extended instrumental and multicentury paleoclimate records. Future changes of El Niño are stronger for CP events than for EP events and differ between models. Models with a projected La Niña–like mean-state warming pattern show a tendency toward more EP but fewer CP events compared to models with an El Niño–like warming pattern. Among the models with more El Niño–like warming, differences in future El Niño can be partially explained by Pacific decadal variability (PDV). During positive PDV phases, more El Niño events occur, so future frequency changes are mainly determined by projected changes during positive PDV phases. Similarly, the intensity of El Niño is strongest during positive PDV phases. Future changes to El Niño may thus depend on both mean-state warming and decadal-scale natural variability.

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Joëlle Gergis
,
Raphael Neukom
,
Ailie J. E. Gallant
, and
David J. Karoly

Abstract

Multiproxy warm season (September–February) temperature reconstructions are presented for the combined land–ocean region of Australasia (0°–50°S, 110°E–180°) covering 1000–2001. Using between 2 (R2) and 28 (R28) paleoclimate records, four 1000-member ensemble reconstructions of regional temperature are developed using four statistical methods: principal component regression (PCR), composite plus scale (CPS), Bayesian hierarchical models (LNA), and pairwise comparison (PaiCo). The reconstructions are then compared with a three-member ensemble of GISS-E2-R climate model simulations and independent paleoclimate records. Decadal fluctuations in Australasian temperatures are remarkably similar between the four reconstruction methods. There are, however, differences in the amplitude of temperature variations between the different statistical methods and proxy networks. When the R28 network is used, the warmest 30-yr periods occur after 1950 in 77% of ensemble members over all methods. However, reconstructions based on only the longest records (R2 and R3 networks) indicate that single 30- and 10-yr periods of similar or slightly higher temperatures than in the late twentieth century may have occurred during the first half of the millennium. Regardless, the most recent instrumental temperatures (1985–2014) are above the 90th percentile of all 12 reconstruction ensembles (four reconstruction methods based on three proxy networks—R28, R3, and R2). The reconstructed twentieth-century warming cannot be explained by natural variability alone using GISS-E2-R. In this climate model, anthropogenic forcing is required to produce the rate and magnitude of post-1950 warming observed in the Australasian region. These paleoclimate results are consistent with other studies that attribute the post-1950 warming in Australian temperature records to increases in atmospheric greenhouse gas concentrations.

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Andrea J. Dittus
,
David J. Karoly
,
Sophie C. Lewis
, and
Lisa V. Alexander

Abstract

This study examines trends in the area affected by temperature and precipitation extremes across five large-scale regions using the climate extremes index (CEI) framework. Analyzing changes in temperature and precipitation extremes in terms of areal fraction provides information from a different perspective and can be useful for climate monitoring. Trends in five temperature and precipitation components are analyzed, calculated using a new method based on standard extreme indices. These indices, derived from daily meteorological station data, are obtained from two global land-based gridded extreme indices datasets. The four continental-scale regions of Europe, North America, Asia, and Australia are analyzed over the period from 1951 to 2010, where sufficient data coverage is available. These components are also computed for the entire Northern Hemisphere, providing the first CEI results at the hemispheric scale. Results show statistically significant increases in the percentage area experiencing much-above-average warm days and nights and much-below-average cool days and nights for all regions, with the exception of North America for maximum temperature extremes. Increases in the area affected by precipitation extremes are also found for the Northern Hemisphere regions, particularly Europe and North America.

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Nikolaos Christidis
,
Peter A. Stott
,
Simon Brown
,
David J. Karoly
, and
John Caesar

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.

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Diandong Ren
,
Rong Fu
,
Lance M. Leslie
,
Jianli Chen
,
Clark R. Wilson
, and
David J. Karoly

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.

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Tahl S. Kestin
,
David J. Karoly
,
Jun-Ichi Yano
, and
Nicola A. Rayner

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.

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Ailie J. E. Gallant
,
Steven J. Phipps
,
David J. Karoly
,
A. Brett Mullan
, and
Andrew M. Lorrey

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.

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